Next Article in Journal
Qualitative and Quantitative Analysis of Heavy Crude Oil Samples and Their SARA Fractions with 13C Nuclear Magnetic Resonance
Previous Article in Journal
Technical Aspects of Biofuel Production from Different Sources in Malaysia—A Review
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

An Overview of Household Energy Consumption and Carbon Dioxide Emissions in Iran

Omeid Rahmani
Shahabaldin Rezania
Amin Beiranvand Pour
Shahram M. Aminpour
Mohammad Soltani
Yousef Ghaderpour
1 and
Bahareh Oryani
Mahabad Branch, Islamic Azad University, Mahabad 59135-433, Iran
Department of Natural Resources Engineering and Management, School of Science and Engineering, University of Kurdistan Hewlêr (UKH), Erbil 44001, Kurdistan Region, Iraq
Department of Environment and Energy, Sejong University, Seoul 05006, Korea
Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, Malaysia
Faculty of Chemical Engineering, Urmia University of Technology, Urmia 57155-419, Iran
Piranshahr Branch, Islamic Azad University, Piranshahr 57816-36163, Iran
Technology Management, Economics and Policy Program, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Author to whom correspondence should be addressed.
Processes 2020, 8(8), 994;
Submission received: 26 July 2020 / Revised: 12 August 2020 / Accepted: 13 August 2020 / Published: 16 August 2020
(This article belongs to the Section Environmental and Green Processes)


This review tends to obtain a deeper understanding of the methods used in household energy consumption and carbon dioxide (CO2) emissions in Iran. Issues relating to energy consumption and CO2 emissions are very complex. This complexity arises from the fact that energy demand and energy consumption in Iran are influenced by many factors, such as income, household size, age, and gender. In Iran, the relevant energy sources mostly include liquefied petroleum gas (LPG) and electricity, which are used for different sectors, such as transportation, industry, and residential. This overview looks at both the theories and empirical studies of household energy consumption and CO2 emissions in Iran. Since energy consumption typically results in air pollution, it is often used as an indicator of environmental degradation. Although Iran is recently faced to energy efficiency improvement from all sectors, household energy requirements have been significantly increased. In Iran, a prime motivator had been improving living standards. As Iran gradually turns into a consumer society, households have an enormous influence on the direct use of energy and related CO2 emissions as well as through indirect use, as embodied in goods and services. The findings of this study can help policymakers to focus on renewable energy projects in order to reduce energy consumption and mitigate CO2 emissions.

1. Introduction

Energy consumption and related carbon dioxide (CO2) emissions are complex issues at different countries because various factors influence energy supply, energy demand, and energy consumption on a global or local scale [1]. It has been demonstrated that high demand for energy consumption is associated with an increased need for the use of fossil fuels [2]. In developing countries, the most important challenges in the sector of energy are environmental degradation with regard to the use of fossil fuels, CO2 emissions, and hard access to modern energies such as liquefied petroleum gas (LPG) and renewable, eco-friendly energy sources [3]. In this regard, the public tendency towards using these resources in different counties has been increased [4,5]. Besides, the rate of urbanization and consequently the number of households have been increased in developing countries, which caused more challenges in supplying necessary energy in different sectors [6,7].
It is now well established from a variety of studies that households impact greenhouse gas (GHG) emissions. Druckman and Jackson [8] declared that the amount of GHG emission depends on the amount of energy consumed in households. Therefore, the households’ behavior should be changed to lessen the issues related to energy consumption and climate footprint. In this regard, waste management is one of the key priorities of environmental policy in reducing GHG emissions [9]. In terms of the consumed energy, the GHGs intensity differs from different sources. For instance, the amount of emitted GHGs from burning natural gas is estimated to achieve less toxic air pollution rather than the pollution from the coal source in power plants [10]. According to Oladokun and Odesola [11], there are two important strategies, which need to be made for reducing GHGs emission, namely energy conservation and change in households’ behavior.
Findings from different lifestyles indicate that households’ behavior impact on energy consumption. For instance, households in the UK and Sweden have, respectively, consumed 21% and 51% of the energy in using dishwashers in 1998 [12]. A comparison of the two results from China and the EU reveals that Chinese households traditionally use cold-water for washing the clothes, whereas hot-water is used in the EU’ households. According to an investigation by Streimikiene and Volochovic [9], substantial differences have been found among nations to be related to lighting, household size, room temperature, and operating hours of commercial building.
From the 1970s, the lifestyle of Iranian households has been pursued to a higher living standard. A report by the Statistical Center of Iran [13] shows that Iranian’s per capita disposal income has been increased from USD 10 to USD 25 from 1990 to 2016, respectively. As far as the disposal income increased, the direct energy consumption in Iran also moved upward 51% [13] from 1.658 8 GJ to 2.505 8 GJ. In contrast to other sectors like industries, the report by Statistical Center of Iran (SCI) shows a decreased rate of direct energy consumption from 16.4% to 10.7% by Iranian households. It seems possible that this discrepancy is due to the low share of household direct energy consumption in GDP [14].
Exceptionally, for the last six decades, Iran has been viewed as a nation with a rapid rate of urbanization, increasing from 31% in 1956 to 75% in 2018 [15]. Although Iran is considered one of the main members in the Organization of the Petroleum Exporting Countries (OPEC), the international sanctions prohibit the export of produced oil and natural gas. Due to easy access to the energy sources in Iran, a considerable amount of these resources are consumed by different sectors of Iranian society [16]. In this regard, the rate of energy consumption and CO2 emission in Iran has been increased by 6.2 and 6.1 times, respectively, in the last six decades [17]. Iran is placed among the top 10 countries concerning CO2 emission (Figure 1). It is encountering a fast urbanization rate owing to social-political transformation and industrialization [18,19]. Taking into account the urban settlement within 1960–2017, the urbanization rate increased 2.2 times in Iran [20]. Hence, it caused a significant increment in energy consumption mainly extracted from natural resources [21].
As a result, Iran’s energy policy is mostly focused on the sector of industries rather than the residential sector. The existing studies on energy consumption are extensive and focus particularly on the economic development and the industrial sector in Iran [4,18,23,24,25,26]. In a study investigating energy intensity in Iran, Farajzadeh and Nematollahi [27] reported that the residential sector is responsible for about 20% and 33% of total energy consumption and CO2 emission in Iran, respectively. Therefore, this overview seeks to obtain data that will help to address household energy consumption and related CO2 emission in Iran. The results from this overview make a major contribution to research in terms of reducing CO2 emission by households.
The complexity of issues arising from energy consumption, environment, and sustainable development, and related CO2 emissions has resulted in the need for a comprehensive study into energy consumption by households in Iran. A study on households’ energy consumption and CO2 emissions in cities is important to a country like Iran with a population of more than 80 million and an urbanization rate of 3.5% [13]. The share of households in the total energy demand in Iran in 2010 has been about 25% and increased to 50% in 2019 [19,28], which makes it the highest compared to other sectors. Against this backdrop, a study on household energy consumption patterns is necessary considering the important role energy plays in human development. The energy demand of other sectors (e.g., industrial, agriculture and transportation) in any economy is better captured than the household sector due to centralized ownership, self-interest and increased level of regulation and documentation compared to the residential sector, which is not well defined [29] However, the adoption of disaggregated data on various energy sources used by households in this study gives room for an inquiry into variations that may occur in the energy consumption pattern between the different socio-economic segments in cities.
In addition, the reasons for the preference of any particular energy source(s) are examined by the study. Another possible area of the study would be to investigate households’ energy conservation. For a developing country such as Iran, much attention has been paid on the supply side whenever the issue of household energy is mentioned. In the context of climate change and the attendant global warming resulting from the unsustainable consumption pattern of humans especially city dwellers, attention is now focused on lifestyles and attitude of people with regard to energy consumption and CO2 emissions. In this regard, this overview examines households’ energy consumption and CO2 emissions in Iran towards energy conservation pattern from households for meeting their energy needs. It is hoped that knowledge about the existing consumption across the household sector will help policymakers in formulating policies that will enhance sustainable energy supply and consumption in the country.

2. The Role of Energy in Economic Growth

The role of energy in economic growth has been a controversial topic in previous studies [30,31]. Traditional growth models treat energy as a common factor that has little effect on economic growth. Some more recent entries to neoclassical growth theory include energy as an essential input besides labor and capital for production [32]. With technological improvement, cheaper and more convenient energy has encouraged the substitution of energy derived mechanical power for work done by animals and humans. Since energy is so pervasive, the importance of energy is neglected by humans.
As a critical driver of economic growth, the availability of energy has always been a hot subject on political agendas around the world. Unfortunately, as a modern necessity, energy is also the key contributor to global climate change as well as local air pollution [33]. To respond to energy security and environmental pollution pressures, energy policies have focused on supply-side issues such as economic structural change, technological improvement, and regulatory management [34]. Subsequently, demand-side policies have been gaining more attention from researchers and policymakers. Sustainable consumption is also gaining momentum within the public debate [35].
Electricity consumption by households in Iran increased by 1.53% per year over the past ten years despite significant energy efficiency gains in household appliances and lighting [13]. According to SCI [13], per capita, household energy requirements in Iran show an average increase of 2.36% per year from 2000 to 2015. During the same period, overall energy intensity per household had increased at just 0.25% per year [36]. In Iran, energy price is again reformed in 2019, which affect all aspects of the economy. One of the most common issues in Iran is environmental change, which is considered an effective parameter in reducing CO2 emissions [37].

2.1. Energy Challenges in an Urbanizing World

Recent trends in energy-related challenges have been grown at all levels of development in the world. Energy challenges also negatively impact on health conditions of residential in cities, crop yields, and forest integrity across the world [38]. Data collected from different levels of regional and national levels of developing countries show that human health has been affected by changes in the human environment, particularly with reference to air and water pollutions in the last two decades [39]. The condition is further compounded by the energy crisis presently affecting Iran, as shown by the international energy sanctions.
In Iran, urbanization plays an essential role in energy policy and planning, and transition from traditional to modern fuels [19]. Nowadays, more than 73% of the population in Iran has been living in cities [40]. Urbanization and industrialization accompanied by the economic development in Iran led to a continuous demographic and extensive growth of cities [41]. Besides, the increasing urban population, competitive pressure due to economic development and scarcity of available resources has led to the introduction of modern energy sources [19]. Increasing energy consumption is related to a growing population and also per capita consumption and changing consumer needs, behavior and lifestyles [42]. Increasing income accompanied by urbanization has led to a rise in household energy consumption. To address this rising trend in energy consumption, policymakers in Iran have tried to enforce energy efficiency improvements for LPG appliances [28].

2.2. Sustainable Consumption

Sustainable consumption is “the use of goods and services that respond to basic needs and bring a better quality of life, while minimizing the use of natural resources, toxic materials and emissions of waste and pollutants over the life-cycle, so as not to jeopardize the needs of future generations” [43]. Since then, this definition has been widely used in the research and practices of sustainable consumption. Sustainable consumption is a site- and problem-specific concept [44,45]. Moreover, Sustainable consumption is considered a dynamic concept that shows only the path of desired or required changes [46]. In general, sustainable consumption is a long-term process involving negotiation and consensus building.
Understanding the decision-making process of consumers is the key to the study of what might constitute sustainable consumption [47]. Different criteria impact consumer decision making, as a complex process, including self-interest motives (e.g., price, income, quality, personal taste, and lifestyle) and social and economic incentives (e.g., culture, self-identity, and environmental and social concerns) [48]. The economic conceptual framework is rooted in consumer preference formation and studies in terms of showing the interaction between consumed goods and services within aggregate consumption [49]. On the other hand, the systems of the provision model of consumption view consumption as an active social process rather than just the sum of individual behaviors [50]. Consumption processes are closed to the systems of production and distribution that facilitate individual lifestyles.
The needs opportunities ability (NOA) model (Figure 2) is the basic model used to identify certain forces underlying consumer behavior at both the macro- and micro-level [51]. Based on the motivations–opportunities–abilities model, consumer behavior is determined by the needs, opportunities, and abilities of people to meet their own requirements [52,53]. Needs refer to a series of intentions that people follow to preserve or adjust their “quality of life”. Energy is required to provide a range of services such as running the appliances in households. Households’ energy needs grow along with increasing demand for higher comfort levels such as more substantial houses, more comfortable indoor temperatures, and higher hygiene needs. Opportunities are a set of external facilitating conditions, such as availability and accessibility of products, services, and relevant information. Abilities are the set of households’ internal capacities to pursue products and services. They include financial (e.g., income), temporal (e.g., leisure time), spatial (e.g., home vacant space and location of the house), cognitive and physical (e.g., health, fitness), and skill (e.g., possess of licenses and permits) abilities [54]. Opportunities and strengths determine a consumer’s level of behavioral control. Besides, Wu et al. [55] demonstrated that observed behavior can positively affect the behavior of sustainable consumer using the model of “sustainable consumer behavior influence factors”. Moreover, findings from Zhu and Guo [56] show that the main influence factors of sustainable consumption are technology, regulations, and attitudes of consumers.
The NOA model is embedded in five social contexts: technology, economy, demography, institutions, and culture. Technology improvement and economic development increase consumers’ purchase opportunities and abilities through mass production and lower prices. Demographic changes yield a multiplier effect on consumption as the population increases. Institutions, especially the government, might constrain consumers’ opportunities and abilities by prices or regulations. Cultural norms and values might also penetrate the consumption process by influencing consumers’ needs and opportunities. For example, consumers’ sense of “quality of life” and their household energy consumption behavior depend highly on their cultural habits and routines [57,58]. The NOA model offers a beneficial structure for analyzing the driving forces of household energy consumption change at both the macro- and micro-level. Moreover, it provides a dynamic framework of consumers’ behavior through consumer’s ability and willingness to change [55].

3. Energy Demand and Consumption in Iran

It is essential to incorporate the energy sources to show better comprehension of the outline of energy consumption by households [19]. More recently, an increasing interest has existed in the matter of energy consumption in Iran, primarily from two sectors of industry and transportation. In the past ten years, Iran has faced a rapid development of industrialization and urbanization leading to a fast acceleration in the energy case. This was mainly caused by the fact that the effect of households’ energy consumption on the environment is not equivalent to those of industry and transportation sectors. Kenny and Gray [59] similarly indicate that the industry and transport sectors have a key role in CO2 emissions in Ireland. Sepehr et al. [60] reported that the household sector in Iran allocates about 40% of total energy demand. Therefore, it will undoubtedly have a higher CO2 emission level and a more considerable impact on the environment [28]. Hence, a tendency exists among people to act in a way to reduce the effect of energy consumption on the environment. The peoples’ decisions and choices of energy consumption are the key factors with a positive or negative impact on people’s environment and welfare [28]. Thus, the thoughts and performances of the people are the main focus of the debate on energy conservation. If the patterns of energy conservation are changed in a society, it is essential to consider the factors enforcing and motivating the individuals in this manner.
In Iran, the government controls the energy pricing mechanism, which has attempted to improve it by founding a market-based mechanism [61]. Because the subsidies are provided by the Iranian government for energy consumption in different sectors such as residential, the energy price is lower compared to global prices. There is less information regarding the lower price of energy caused by the association between energy consumption and CO2 emission by households. There is presently no established system that computes and reports directly and indirectly the total household CO2 emissions. Nevertheless, over the past twenty years, energy consumption has been increased constantly in Iran residential sector [13]. From the residential sector, the total energy consumption in Iran has marginal incremented per capita from 8.1 to 1.3 kJ in 2011. This is due to the use of a central air-conditioner, dishwashing machine, washing machine, and numerous buildings remarkably occupied [13].
From Figure 3, CO2 emissions in Iran show an increase of nearly 5 times since 1971. Also, the residential sector is responsible for the same trend of direct carbon dioxide emissions increased by the rate of 2.45 times from 1990 to 2011. This considerable increase in household energy consumption has caused a renewed policy by the Iranian government to decline this trend in the last decade. One prompt measure that has been taken is a gradual decrease in energy subside and an increase in energy price. This renewed policy led to a decline in energy demand, and subsequently, the growth rate of oil and electricity consumption fell to −15% and −8%, respectively [62].
Iran, as one of the top 10 countries in CO2 emissions (see Figure 1), is the main owner of oil and natural gas resources and tries to resolve most of its national energy demand by preparing and using fossil fuels. In recent years, there has been an obvious turn from oil products to natural gas [64] and now around ¾ of overall energy consumption is covered by the residential sector [13,27]. Iran is a vast country with different environment-friendly natural energy sources such as solar energy or wind power [36]. Current programs lead to an increasing rate of CO2 emissions, and still, there is no clear and significant plan for applying clean energy sources [37].

4. Household Energy Consumption and CO2 Emission

A considerable portion of energy demand and consumption in the world is used in cities. An estimation analyzed by the International Energy Agency [65] shows that the world has experienced about two times the growth of energy consumption from 2010 to 2018. In this regard, about 80% of energy consumption has been attributed to cities. From Figure 4, energy consumption and related CO2 emission show a considerable increase in CO2 per capita from 7.64 metric tons in 2010 to 8.87 metric tons in 2018 [63]. The urbanization caused to significantly increase the CO2 emissions in cities by shifting energy sources [65]. More recently, there has been worldwide recognition of the problems associated with greenhouse gas (GHG) emissions within and outside of city boundaries [66,67,68]. Along with the growth of energy consumption and GHG emissions, however, there is not a direct relationship to recognize energy consumption as an indicator of GHG emissions [69]. According to Mondani et al. [70], energy indicators are categorized as energy use efficiency, energy productivity, specific energy and net energy. Energy consumed in cities can be produced from different sources, including electricity and fossil fuels, each with a different climate footprint [65]. Although the efficacy of produced energy influences the amount of GHG emissions, the energy consumption impacts on GHG emissions through the amount of energy consumed, GHG intensity, and GHG emissions factor [71]. It is therefore likely that a significant preeminence should be made between energy consumption and energy supply [72].
There are five primary sources for CO2 emissions in Iran (Figure 5), in which industry and household sectors are responsible for 24.1% and 23.4% CO2 emission, respectively. The results are in accordant to the globally main source of industry sector, which produces more than one quarter (26%) of total carbon emissions [73]. As shown in Figure 5, the second primary source is households with 23.4% of CO2 emissions. The transportation sector is emitting 21.2% of total CO2 emissions, and other sectors is considered as the sources of 31.3% of total CO2 emissions in Iran. So, households are directly the producer of about one-fourth of Iran CO2 emissions. With a combination of transportation and household sectors, 44.6% of CO2 emissions have resulted from urban and rural communities. In another way, the residential sector represents an essential role in the effort for emissions reduction, and households are thus a talented group when addressing energy conservation [74]. A consensus exists among scholars and planners at a neighborhood level indicating that household CO2 emission is related to the density, accessibility to employment, land-use combination, and vicinity to the public transit [75]. Iran’s carbon emission will increase to 15.1 metric tons and 9.5 metric tons CO2 per year without and with countermeasures, respectively, by 2030 [15]. Iran’s CO2 emissions have been increased by 5% annually between 1990 and 2016, whereas the global average for the same period is 2.3% [65].
Information, motivation, and responsibility are the basis for the household’s quantity of CO2 emission [8]. Some factors affect several instruments including incentives for energy savings, information given by campaigns, energy consultancy, workshops, feedback projects, and publications [77]. Considering the essential role of general public opinion in triggering the consumer’s performance, the mentioned factors can significantly contribute to the individual energy savings and increasing the interest and knowledge regarding energy consumption [78]. Learning about the costs and performance of energy-efficient technologies is particularly challenging since their benefits are usually not directly observable. For instance, an energy bill is typically given for the households in Iran providing no individual end-uses breakdown as well as no information on GHG emissions. Proving sufficient feedback to consumers regarding their energy use and on the potential effect of their efficiency investments is essential [79].

Rebound Effects

Rebound effects partly explain why total household energy consumption rises when the energy efficiency of the production sector improves [80,81]. With the existence of a rebound effect, energy efficiency policies may serve the goals of promoting economic growth. However, energy efficiency improvement may lead to more resource use in absolute terms. Rebound effects are composed of four levels. The first level is the price effect. For producers, increased productivity reduces production costs and theoretically enables an increase in supply. The second level is the income effect. When costs per unit of output fall, consumers can afford more energy-intensive products and services. Wilhite [82] points out that the introduction of new technologies may at the same time create new energy-intensive practices. The third is a replacement effect. As the share of energy use in total expenditures reduces, the demand for other goods and services rises, including goods and services that have a significant share of energy embodied in their production. The increased demand for new products and services requires more energy. The last level is a transformation effect. Fuel efficiency and other technological improvements alter human activity through changes in the allocation of time [83].
Household energy consumption increases in both developed and developing countries, even with significant energy efficiency improvements and strict energy price regulation [78,84]. However, this theory only concerns technological change and neglects the impact of changing consumer demand towards higher living standards [57]. Sustainable consumption indicates a research framework as well as directions of desired changes in household consumption behavior [58]. Empirical studies examine household energy consumption patterns of specific countries or regions and explore critical factors that affect household energy consumption [19,85].

5. Influential Perspectives on Household Energy Consumption

The following subsections discuss the current research that explores critical drivers of household energy consumption from three perspectives: (1) factors from the production-side view, (2) factors from the household-side view, and (3) methods used to examine these factors.

5.1. Production-Side View

To comprehend the changing trends of energy consumption, decomposition analysis is normally utilized owing to a set of crucial comparable economic forces. There are changes in energy consumption normally categorized into structural changes and efficiency improvements [35]. Efficiency improvements reduce energy use to produce a product within a particular sector, while structural change is an alternative in the share of economic activities among industries (e.g., shifting from energy-intensive activities to less energy-intensive activities). Mulder and de Groot. [86] found that the structural change causes nearly all the decline in energy intensity in Norway and about half of the decline in Japan from 1970 to 2005.
Most of the decline to structural changes are attributed to early discussions of post-1978 changes in Iran’s energy intensity. Fundamental decomposition analysis was utilized by Sabetghadam [87] to test the energy use of Iran within 1981–1987 and indicated that all of the energy intensity reduction was nearly related to the technical change, however, the structural change accounted for a slight increment in energy intensity. The research boundary was further extended by Sabetghadam [87] for 1987–1992 and similar results were obtained. The study indicated that most of the drop in industrial energy intensity in Iran is related to energy efficiency enhancements. Sabetghadam [87] supported that efficiency improvements are the main contributor to aggregate energy intensity reductions but found further that structural change contributed little to energy intensity change. Studying structural change at different levels of aggregation may also yield different results.

5.2. Household-Side Analysis

Energy consumption behaviors that contribute to increasing energy demand are different in developing versus developed countries. In Mexico, cooking, water heating, lighting, and electrical appliances are the fast-growing end uses [88,89,90]. In Iran, water heating and space heating dominate the set of fast-growing energy end uses among households [28,36,91]. A typical individual’s consumption choice in modern society no longer has much to do with basic biological needs such as food or shelter [92,93,94]. Individuals’ choices concerning the amount and quantity of recreation, amusement, housing, food, and other consumables are linked to past or familiar experiences, cultural norms, peer influences, and other social influences, such as those from the media [95]. In this regard, De Almeida et al. [96] stated that entertainment loads and information technologies are crucial contributors to electricity demand. Compared to other countries with similar climates, the households’ demand shares for energy via space heating and cooling are rather low in Iran.
On the other hand, energy efficiency improvements can be locked-in to economies through changes in technology and infrastructure—the capital investments identified in the preceding paragraph. Infrastructures such as housing stock and public transportation parts of the hardware have an effect on the pattern of energy consumption. Household energy consumption is strongly influenced by dwelling type and age, surrounding structures, and other housing characteristics, such as insulation and building regulation [78]. Using double-glazed windows and more energy-efficient home appliances contribute to household energy conservation [85]. For instance, through such technological innovations, the energy consumption of dwellings built in the Netherlands is lowering after 1996 [97]. Interestingly the technological innovations are not always costly. A study conducted by Ürge-Vorsatz et al. [98] indicated that 32–33% of global building energy consumption for space heating can be conserved at a relatively low cost in the existing housing stock.
In Iran, urbanization is associated with both higher income and higher household energy consumption [19]. Moreover, increasing urbanization levels will lead to increased adoption of electronic home appliances, lighting, and other amenities [99]. Urban residents’ lifestyle changes further causes a growth trend of energy consumption. In developed countries, households in high-density areas are less energy-intensive [100]. Norman et al. [101] conducted a lifecycle analysis on two residential settlements with different density levels in Toronto and found that residents in high-density areas consume less than half of operational energy and produced half the greenhouse gas emission compared to their counterparts who live in low-density areas [101]. According to Ewing and Rong [102], households in sprawling regions are more likely to live in large, detached single-family houses, which consume more operational energy than do single-family houses in compact areas.
Furthermore, cultural and traditional attitudes toward certain goods and behaviors can influence energy consumption preferences by households for a country such as Iran. Culture and traditions are typically passed from generation to generation and are primarily bestowed upon an individual over his or her lifetime. Abrahamse and Steg [30,103] suggest that socio-demographic factors such as income and household size shape households’ opportunities and basic needs for energy, while reductions in energy use require a conscious effort—a change in households’ behaviors [104]. Wilhite et al. [105] compared energy use behaviors in Japan and Norway and found very different space heating, lighting, and hot water use between the two countries. Not surprisingly, higher hot water usage in Japan was caused by the bathing habit in Japan (both publicly and privately), which had deep cultural roots [106]. Moreover, Norwegians tended to be more energy insensitive during lighting and heating, as they have less annual sunlight and a colder yearly average temperature [107]. Perhaps more surprisingly, however, is that Norwegians heated much of their living areas most of the time, while the Japanese tended to heat only rooms they were immediately using. Low energy prices in Norway may explain some of this discrepancy in behavior. But Sovacool and Griffiths [107] suggested that Norwegians also may have a cultural, physical, and psychological affinity for having all rooms heated.

5.3. Used Methods to Analyze Household Energy Consumption

Input–output (I–O) analysis is frequently used to calculate the total energy requirements of households [108,109,110,111,112,113]. Kok et al. [114] summarized three types of I–O analysis used by researchers. First is fundamental I–O energy analysis. This method is based on data for the production side of the economy and is useful for describing the environmental impacts of a specific country or comparing differences among countries. The second method combines I–O energy analysis with household survey data. In this method, the consumption data is from household expenditure surveys rather than I–O tables. It combines the energy intensities of different economic sectors or commodities with household expenditure survey data. This method can generate more information on the household level and compare the energy requirement of different household types. The third method combines life-cycle analysis with I–O analysis. It leads to more detailed information on both the production and consumption side. It is good at describing and explaining environmental impacts at the household level.
Based on the primary I–O method, Hajilary et al. [115] calculated CO2 emission from Iran’s household consumption and resulted that the household sector accounted for about 36% of total CO2 emissions generated through primary energy consumption (Figure 6). The results from Hajilary et al. [115] indicate that rising total population, urbanization, and household consumption per capita contributes to the growth of indirect CO2 emissions while reduced carbon intensity mitigates the increase in CO2 emissions.
Bin and Dowlatabadi [92] proposed a consumer lifestyle approach (CLA) as an alternative paradigm to show the relationship between household consumption behaviors and their environmental impact. Their findings revealed that about 80% of the energy consumption and related CO2 emissions are in accordance with energy demand in the USA. In CLA, the consumer’s decision making is affected by five interacting groups: (1) external environmental factors (e.g., traditions and technology levels); (2) individual determinants (e.g., attitudes and personal preferences); (3) household characteristics (e.g., housing size, household types and size, and household income); (4) consumer choices (e.g., information and availability of goods and services); and (5) consequences (e.g., consumption related material, energy use, and environmental impacts).
Based on CLA, Soltani et al. [28] compute the direct and indirect energy consumption and related CO2 emissions of Iranian households in 2016. Household energy consumption was responsible for about 26% of total primary energy use and about 30% of total CO2 emission in Iran. They also compared household energy use and CO2 emission for different income levels. Soltani et al. [28] found that income has a considerable effect on the amount and structure of household indirect energy use in Iran. High-income households have a tendency to consume more energy indirectly through goods and services and have a more diversified indirect energy consumption structure.
Micro-level survey data are also widely used in determining household energy requirements [36,116,117]. Combining India’s national household expenditure survey data with the estimated energy intensities of production sectors, Pachauri [116] quantifies household direct and indirect energy requirements at the household level in 1993. Pachauri [116] also explores the critical influential factors of household energy requirements through multi-variable regression. Based on the same dataset for the year 1999, Narasimha Rao and Reddy [118] use a multinomial logit model to analyze critical factors that affect households’ energy choices for cooking and lighting in India. Moreover, Gould et al. [119] discussed the role of education and attitudes in cooking fuel choice in India and concluded that education is a strong predictor of LPG adoption. O’Neill and Chen [120] employed the residential energy consumption data to test the variables that affect the household energy use in the USA. O’Neill and Chen’s study reveals that some demographical factors, particularly household size, have a substantial influence on household energy use.
Recent micro-level studies of household energy consumption in Iran are at the city level [19,121]. Since Iran does not have national surveys that collect energy-related data for households, studies conducted by Soltani et al. [19] and Sadati and Edwards [121] on household energy consumption are at the micro-level. Thus, there appears to be room for micro-level studies that could contribute to a complete understanding of critical factors behind various household energy consumption patterns. Moshiri [36] indicated that micro survey data at the household estimate demands for energy consumption. An advantage of micro-level over the macro-level analysis is that the micro-level can control for the characteristics of households, and makes use of detailed information on capital and its utilization [36].
Knowing the essential elements that influence energy consumption has become very considerable [122]. In the supply-side view, a change in energy consumption is often decomposed into changes in energy efficiency, production input structure, consumption structure, and consumption level. Household-level analysis studies the effect of climate, socio-economic, social demographic, social-cultural, and behavioral factors. Environmental and socio-economic factors significantly affect households’ energy consumption. The impact of demographical change, cultural difference, and behavioral models are also examined in some empirical analyses and conceptual frameworks. The theoretical and modeling origin for the explanation of household energy consumption is “the energy ladder” theory. Moreover, “energy services” and “energy mix model” theories are considered.

5.3.1. Energy Ladder

The relationship between the increase in household incomes and the fuel choice for energy consumption is modeled within the energy ladder [123]. In the hypothesis determining the energy ladder theory (Figure 7), it is indicated that the energy source choices of the households can be classified between the most and the least technologically advanced energy sources either in descending or ascending mode. Such a hypothesis controls the households’ energy source choice in Iran. As such choices become more financial, they will choose more sophisticated energy sources [19].
The energy ladder model consists of three stages. The first stage, known as the traditional stage, refers to the period where the level of reliance on traditional fuels such as dung, wood, and agricultural is high. The second stage, known as the transitional stage, is the stage when households start using kerosene and coal. In the third stage, known as the clean stage, households use clean and efficient energy sources such as electricity and LPG [125]. The lowest rung of the energy ladder comprises traditional energy sources such as dung, crop residue, fuelwood, and charcoal. The middle and high rungs of the ladder include transitional energy sources such as kerosene, LPG, and electricity, respectively [126]. Betterment of households’ financial circumstances and their ability to spend more on energy constitute the main drive of upward movement from the lowest to highest rungs of the energy ladder model [125]. When it comes to the relationship between technology and energy ladder model, one can find the connection between the model and technological advancements, efficiently devised energy-consuming appliances, utilization of clean energy sources and reduction of CO2 and other GHG emissions.
Heltberg [127] believes that identifying the direct and significant impact of households’ income on their energy sources is the key accomplishment of energy ladder model. This model acts in both macro- and micro- levels, in which the model implies the relationship between a country’s developmental level and energy consumption at the macro-level and it signifies the correlation between households’ incomes and their energy source selections at the micro-level [19]. The micro-level of energy ladder model also demonstrates the fact that households tend to move to higher rungs of the model and a result adopt more technologically sophisticated and clean energy sources not because they heed to environmental concerns but because they want to display their socio-economic superiority and betterment to each other. This is notwithstanding the households’ income, the price of energy sources and energy-consuming equipment, and the availability of energy sources’ impact on households’ tendency to change their preference towards energy sources [19].

5.3.2. Energy Services

Energy services implying the advantages of using a particular energy source are quantifiable in temperature, work, or heat units. Modi et al. [128] argue that energy services indicate the advantages of a particular energy source facilitating human beings’ welfare. Energy services have multi-variegated nature; thus, one service and benefit can lead to realizing other services and benefits [129]. For instance, a warm bath can have hygienic, clinical, and therapeutic benefits for a person on various occasions. In the rural areas within developing countries, people use biomass energy sources to produce requirement energy, which will be spent on cooking, lighting, and heating. Haas et al. [130] indicate that energy services should be oriented by the inputs, users, domains energy sources, and outputs. It should be stated that this model can be sectorally classified, indicating considerable differences between energy services of household, commercial, transportation, and industrial sectors.
Energy services in cities are classified into low, medium, and high categories. Regardless of a sufficient number of services, low-income households consume a further amount of energy [131]. For households that are categorized in medium and high categories, a sufficient number of services are available. Cooking, lighting, heating, and appliance are identified by Floor and Barnes [132] as the respective domains with aggregated energy services. Dutschke et al. [133] regarded radio, television, lighting, heating, and cooking as the domains of energy services of low-income households. In Africa, water heating, lighting, cooking, radio, television, and refrigeration are the domains with the highest number of materialized energy services [134].
Electricity, natural gas, coal, LPG, and kerosene are the energy source choices of the middle class. These choices have a higher number of energy services. These energy sources are used for cooking, heating, lighting, and entertainment. Sovacool [129] believes that cost economization of energy sources and their transformation into efficient forms encourage the middle class to spend more on energy. In another study, Fouquet [135] mentions that lighting, heating, power, and transportation are the four key areas in which energy services are aggregated. Furthermore, Fouquet [135] believes that the cost reduction of energy sources results in consumption increase.
Sovacool [129] shows that both the middle and upper classes of society have access to technologically advanced energy sources; however, the latter uses more energy. The reason for using a higher amount of energy is that they have more luxuriant and affluent appliances, such as a heated swimming pool, multiple kitchens, and air conditioners [129]. This is not the case for the low-income category since households save their energy consumption as they cannot afford to spend more. They cannot spend energy extravagantly unlike the upper class. Loveday et al. [136] embodied the distinction between these two opposing attitudes regarding energy consumption by households as a low-income category tends to wear more clothes in winter rather than the upper-income category. On the other hand, low-income households do not have the privilege of selecting any desirable energy source for the purpose of heating the house.
Social signaling and conspicuous consumption are two newly coined terminologies through which social scholars describe the relationship between energy consumption and social class. These terms also show the correlation between capital, technological advancements and households’ level of comfort and convenience [137]. Unlike the energy ladder model, the energy services model does not consider a linear developmental path for selecting energy sources by households. This model sees households’ reaching to one single preferential energy source impossible and emphasizes the simultaneous utilization of multiple energy sources [129].

5.3.3. Energy Mix Model

Energy Mix Model, also known as Fuel Stacking, believes in households’ combinatory usage of energy sources. Despite the energy ladder model, the energy mix model does not rely on households’ linear advancement towards any particular energy source. Using many energy sources has turned into a prevalent energy consumption strategy for households in developing countries [138]. The energy mix model believes that variables such as socio-demographic variables affect the household’s adoption or dropping of an energy source. Furthermore, the model emphasizes that in their daily usage, households have the tendency to complement their energy sources with each other. When these sources complement each other, the energy mix model considers various socio-economic and cultural factors that result in the realization of such complementary energy fusion.
Heltberg [127] observes that the energy mix model does not believe in an ascending movement from one kind of energy source towards another kind. Heltberg [127] maintains that adopting a new energy source (e.g., commercial fuels) by a household does not necessarily result in abandonment of previously used sources (e.g., dung, fuelwood, charcoal). In developing countries, one of the areas where energy sources are used in a comminatory fashion is cooking [139]. Barnes et al. [140] believe that energy consumption patterns have been transforming in time and the best way to comprehend such changing patterns is to see them in developing patterns than seeing them in exclusive shifting stages.
Energy mix models believe that all the social classes of society use energy sources in combination. This observation is not in accordance with the energy ladder model that only believes in direct correlation between households’ economic circumstances and the type of energy choice they will have for their routine usages. The energy mix model sees households choose their energy sources through a portfolio or menu. The options of this portfolio or menu can be arranged either based on economic, cultural, and personal criteria [141]. Heltberg [127] emphasizes that the options of such portfolios or menus can be categorized because of their size, composition and diversification.
In their studies on Thai urban communities, Nansaior et al. [142] reach to the conclusion that although urbanization causes the prevalence of clean energy sources and decline of biomass fuels in the options of household energy portfolios, urbanized Thai households still use traditional fuels and energy sources such as biomass fuels. Such a combination of traditional and modern energy sources proves the combinatory premise of the energy mix model. Both the energy mix model and energy service ladder challenge the validity of all the claims of the energy ladder model [142].

5.4. Critique of the Energy Ladder Model

As mentioned earlier, the energy ladder model has been challenged by various models and the works of scholars and researchers. It is found that owing to the betterment of households’ income in most developing countries, they do not essentially move towards the ideal sources. In other words, the energy choices of the households cannot be placed in a linear continuum with the lowest end including dung, fuelwood and crop residue, and the highest-end including electricity and LPG. In the energy ladder model as an exclusivist model, the emphasis is only on determining the role of households’ income within their energy source selections. Although the key role of households’ income in their energy source choices should be acknowledged, the impacts of other factors on households’ energy source choices should be considered as well.
Chambwera [143] observes that while energy price and households’ income impact the amount and the type of energy that will be consumed by them, there are other cultural factors that can undermine the choices households should have based on the economic circumstances. In other studies, it is proven that even in cases where households can afford clean and technologically advanced energy choices, they may choose more traditional sources because of their cultural affinities. This shows that, in some cases, the brunt of cultural and personal preferences in choosing household energy sources is higher than the influence households’ economic circumstances are going to have.
In the study in India, Joon et al. [138] report that the reason that India has not experienced a complete movement towards clean fuels and more advanced energy sources are not economic and it is needed to be sought in the socio-economic traits of the Indian society. When it comes to cooking, Indians do have taste preferences that can be accomplished through the usage of more traditional energy choices. That is why, while they use LPG stoves for preparing tea and vegetable, more traditional stoves, which are called Chulah (s), are used for baking bread. They think that such stoves will make the prepared bread crunchier and more delicious.
In the study by Mekonnen and Kohlin [144], it is observed that the urbanized Ethiopian households use multiple energy sources. This multiplicity has very little to do with either an increase or decrease in their income and, as a result, undermines the linear expectation of the energy ladder model. In addition to the lack of correlation between the Ethiopian households’ incomes and energy source choices, it is observed that each society regards a kind of energy luxurious and does not see a single technologically advanced fuel or energy source as being the ultimate luxurious choice. In Ethiopia, wood and charcoal are conventionally considered as traditional and inferior energy choices because households can spend most of their income on such energy sources. In another study in Ethiopia, Gebreegziabher et al. [145] believe that all energy sources are interchangeable with each other. The multiplicity in households’ energy source choices attests to the versatility of their lifestyles and food habits.
A study shows that, in Mexico, people do not yearn linearly to accomplish a single type of fuel or energy source and use various fuels and energy sources combinatorially. Most Mexican households use fuelwood and LPG simultaneously for maintaining their daily energy requirements. Some households even start using more traditional energy sources and cooking appliances not because of deterioration of their financial circumstances but because of the impact of cultural and personal factors [141]. All the aforementioned factors attest to the fact that the emergence of a new technologically advanced energy source, e.g., LPG, does not necessarily put an end to the usage of more conventional sources in households. In most cases, households tend to diversify their energy source choices and fuels rather than relying on one particular fuel or energy source [142].

6. Influential Factors on Household Energy Consumption and CO2 Emissions

Research on household energy consumption not only focuses on energy directly consumed by households but also energy embodied in the goods and services consumed by households. Both aspects of household energy consumption in direct and indirect CO2 emissions were studied in several studies. For example, in the study of Brizga et al. [146], the connection between CO2 emissions and households’ energy consumption in the Baltic States was evaluated. A model of households’ energy consumption was introduced by Dai et al. [147] that affects the CO2 emissions in China. Zhang et al. [148] in another study in China observed that altering lifestyle, from traditional to modern, increased the CO2 emissions in Chinese households. Barr et al. [149] assessed the factors relevant to the habitual and purchase-oriented actions that influence the rate of energy conservation in the UK. Correspondingly, Trotta [150] took into account the role of demographic, environmental, and residential circumstances in the UK’s household energy consumption. Soltani et al. [19] reported that numerous variables affect the pattern of household energy consumption such as household size and age, educational and income level, and gender. In this concern, household energy consumption is increased by increasing energy demand as a result of population growth in Iran. Soltani et al. [28] evaluated the factors affecting Iranian household energy consumption and related CO2 emissions (Figure 8). The share of energy conservation depends on five main factors of income, educational level, household size, household age, and gender [28]. Variations mainly depend on differences in income levels of households.
The pattern of households’ energy consumption is mostly based on income level, human behavior, and geographical area [151]. Weather and wealth partly determine the change across countries in the quantity of energy utilized per capita, which is large both at the household and economy levels. This is also properly credited to various lifestyles. Income and household size are the main determinants behind the changes in the energy requirement. While more direct energy (for example electricity, motor fuel, and natural gas) is relatively required by the lowest income classes, higher, more indirect energy (for example the energy needed for the production of food) is required by income classes in absolute and relative terms. For instance, for holidays the energy requirement is very sensitive to the rises in income. On a household level, a large spread exists in the energy requirement, even within a class with the same household size and income.
Income is well-known to have a significant and positive effect on household energy consumption. Income affects household energy use behavior in several ways. On the one hand, it influences the level of consumption via income elasticity, which measures the responsiveness of the quantity needed for the energy source to a change in income [28]. That is, rising income leads to increasing demand for more electronic appliances and more substantial houses [152]. In the case of appliances, demand for time-saving devices rises with increases in opportunity costs for time, which requires households to use and purchase more energy [153,154]. For example, from 2000 to 2017, the average household energy requirement rose by 53% in Brazil [155]. Moll et al. [156] compared the average household energy requirements for different income groups of households in the Netherlands, the United Kingdom, Norway, and Sweden and found that different levels of income yield different energy consumption behaviors. As a result of these investigations, households with a low income tend to spend a higher portion of their costs in the use of electricity and heating the house; however, they do not prefer to spend a further portion of their cost for transportation and recreation.
On the other hand, income also affects the diffusion of energy-efficient equipment. Higher-income gives consumers the ability to invest in energy-efficient appliances and other rather expensive energy conservation techniques such as better insulation and double-glazed windows. Wealthier households tend to replace older and less efficient appliances more frequently [157]. Moreover, higher income is often linked to higher levels of education and thus to greater environmental information access as well as greater environmental awareness. All of these factors are important in energy conservation. However, Cayla et al. [158] indicate that rich households may lack interest in energy-efficient equipment when the budget share dedicated to such an item is small.
Homeownership also plays a vital role in household energy consumption, especially in rented dwellings. Households in rented dwellings tend to consume more energy if heating costs are included in the rental rate [159]. Homeownership is often related to specific durable products and services as well, which can also influence household demand for energy services. In Iran, renters use less electricity than homeowners simply because they own fewer electric appliances, such as refrigerators, washing machines, and air conditioners [36]. Moreover, the mobility of a household, which is associated with homeownership, can influence households’ ability and willingness to invest in energy-efficient appliances and sustainable building design [160].
Other factors, such as climate, technology, infrastructure, energy price, and existing policies, also play an essential role in influencing household energy consumption. Of these, household energy consumption probably varies most with local climate conditions. Places with moderate temperatures such as coastal California have much lower energy consumption levels than sites with temperature extremes like Texas. A typical household’s carbon emission is 78% higher in Memphis than in San Diego [161]. Cheng and Li [162] found that household energy consumption is strongly related to heating degree days (HDDs). This relation between household energy consumption and HDDs is also reported in China, Japan, the USA, and Canada [163], Turkey [164], and France [165].
Energy prices also influence household energy consumption in Iran [19]. They are positively correlated with sustainable energy use: higher energy prices result in reductions in energy use [154]. Moshiri [36] suggests that the effects of the energy price reform on household consumption in Iran cause households to be more responsive in terms of their energy use. However, generally, household energy demand is perceived to be relatively price inelastic since it has become a modern-day necessity. Moreover, the price elasticity of energy demand is asymmetric. Consumers seem to be more sensitive to energy price rises than to energy price declines. The asymmetry is probably due to medium- to long-run adjustments to the higher energy prices by making capital investments that induce more-efficient energy use as well as to expectations of future costs rise.
Demographic factors also substantially affect household energy consumption through ownership and choice of appliances and household behavior. Several studies explore the influence of key demographic factors, such as population size, age structure, household size, gender, and urbanization [27,28,123,166]. Farajzadeh and Nematollahi [27] found that some demographic factors, particularly household size, had a substantial influence on residential energy use in Iran. Households with fewer members tend to use more energy per capita than do larger households. Shifting from a one-person to a two-or-more-person household yields reductions on the order of 20% in household direct energy use per capita in Iran [27]. Aging also influences residential energy consumption since energy consumption tends to change over one’s lifespan. Elderly households tend to be more energy-intensive than are other households. Farajzadeh and Nematollahi [27] also suggest that decisions made by an aging population could disable the ability of the Iranian government to meet its energy-saving targets. Soltani et al. [19] found that income is the leading variable for households in Iran to consume LPG and electricity while other variables remain constant.
Besides, as argued by Soltani et al. [28], there is no any statistically significant relationship between gender and energy conservation. In this regard, Martinsson et al. [167] found that there is no difference between men and women for their pro-environmental behaviors. Trotta [150] explored the way in which the gender does not determine who is more pro-environmental but the person who is expected to watch out the household activities.

7. Household Energy Consumption Pattern in Iran

It is essential to know about the leading factors that affect households’ energy consumption. Wiesmann et al. [168] reported that it is required to incorporate energy sources and the energy demands and needs of households. It can be useful to attain a clear trend in terms of energy choice and energy consumption in an area. As discussed in Section 5.3, several techniques have been introduced to develop a suitable basis for clarification of household energy consumption. In this regard, the energy ladder model was considered as the origin model to elucidate household energy consumption [123]. However, the energy ladder model has been forcefully argued in recent years by scholars [169,170,171]. In Iran, such a hypothesis (i.e., energy ladder) rules the source of energy for households since the further they go financially, the more modern the energy sources they choose [19]. However, there are certain disadvantages related to the use of the energy ladder model in Iran. In this regard, an increase in income of households does not lead to consuming a modern energy source. Otherwise, there is not a direct relation to consuming traditional energy such as fuelwood and crop residue by the low-income category and modern energy such as LPG, renewable, eco-friendly energy sources by upper-income category.
Besides, empirical work testing the rebound effect theory fails to recognize from the following point in Iran. Most of the studies only focus on the technical change that affects energy efficiency [36]. The effect of shifting consumer demand to higher living quality is missing. Understanding such shifts in consumer demand is very important, especially for developing countries like Iran. As stated by Fuerst et al. [151], the energy consumption pattern of households is based on the income level, human behavior, and geographical area. The weather and wealth can clarify the changes within the countries in the quantity of energy utilized per capita, which is large both at the household and economy levels.
Household energy consumption has been increasing in both rural and urban Iran [18]. Figure 9 shows a trend of specific energy consumption (SEC) in Iran, based on electricity from 1999 to 2017 [172]. Household energy consumption barely met basic human needs, such as cooking. Starting in the 2000s, the cooking fuel shortage is gradually solved via this period’s rapid development of agriculture, which enables the availability of more straw and other grain stalks. At the same time, rural households started to use more commercial energy, meaning that electricity became increasingly available in the rural marketplace [173]. Urban households went through a similar transition, from an energy shortage to enough energy to meet basic needs and then further to demand over basic requirements. Compared to their rural counterparts, urban households devote much larger shares of their consumption baskets to commercial energy.
During the process of energy commercialization, energy consumption in households changed dramatically. In Iran, the share of electricity increased significantly from 53 to 270 TW h between 1999 and 2017, respectively (Figure 9). In this period, annual per capita energy consumption based on electricity increased to 0.95 terawatt-hours (TW h). The transition of fuel composition could be partly explained by households’ intensifying share of direct energy use among all household purchases. In urban Iran, the share of delivered energy used for cooking and heating decreased 12%, while the share of energy used for lighting, cooling, and electric appliances increased 12% [173]. After the revolution in Iran in 1979, the primary fuel for cooking and hot water has gradually switched from coal to LPG in urban Iran.
Many studies indicate that household direct energy consumption also varies tremendously across Iran [18,36,173]. Per household CO2 emissions in central Iran are 69% higher than the national average level. However, in the cities of Western Iran, per capita, household CO2 emission is 17% lower than the national average. Rural households in the northeast, south, and southwest Iran tend to be more likely to use biomass than are other regions.
Although Iran had already developed a legal foundation in support of energy efficiency for residential buildings and home appliances [60], its efficiency standards were relatively low compared to those applied in western developed countries. Perhaps more critically, Iran’s monitoring and enforcement of energy efficiency standards and labels were relatively weak. Few policies directly targeted consumers’ energy consumption behaviors. Still, in the summer of 2007, Iran’s state council required all government agencies to set their thermostats no lower than 26 degrees Celsius in public buildings. Nonetheless, this environmental edict was largely ignored by provinces and cities outside of Tehran and even some local officials within Tehran [60]. Household-targeted policies about rational energy consumption remain a missing component in Iran. Thus, a study of households’ energy consumption behavior is undoubtedly significant to inform energy policy.

8. Conclusions

An attempt has been made in this study to review relevant literatures on household energy consumption and related CO2 emissions in Iran with the view to establishing the gap that exists. The nexus between household fuel choice and consumption has been a subject of academic scrutiny by researchers over the years. Most studies of household energy consumption in Iran focus on direct energy consumption factors such as the transition of the fuel mix and changes in end-use structures. The reason for varied opinions on which factor influences on household fuel choice and consumption is borne out of the fact that issues relating to household energy are complex and mostly context specified. Available empirical evidence on the relation and causality has failed to provide a conclusive answer among researchers from various disciplines.
Studies of total energy consumption (both direct and indirect) are mainly macro-level analyses based on I–O models. The indirect energy consumption patterns of urban and rural households are calculated and compared on both national and regional levels for specific years. However, a long-term and spatial analysis of household energy consumption in Iran is missing. These studies cannot display a clear picture of how lifestyle changes from poverty to a relatively well-off society have impacted household energy consumption in Iran. Some studies use household survey data to explore household energy requirements. Most of them are descriptive analysis of one or several cities or counties. Few studies examine the driving factors that impact household energy requirements at the micro-level. Thus, there appears to be room for improvement that could contribute to temporal and spatial analysis in the macro-level as well as micro-level analysis to form a complete understanding of household energy consumption in Iran.
Most of the studies that have been conducted on household energy focused only on a single fuel source, even when multiple fuel sources were taken into consideration; some were excluded. Household energy should be considered as a menu or portfolio in which households make use of various fuels for different purposes in meeting their energy service demand. Income is seen as the major driver in household fuel choice and consumption in most of the studies while other factors have been given little or no attention. Although income plays a major role in determining household fuel choice and consumption, taking into consideration the influence of other factors in response to energy consumption and preference will allow for comparison. However, all the likely factors that influence household fuel choice and energy consumption are examined with the view to establishing the role played by each factor in a developing country such as Iran.
Moreover, none of the existing studies in Iran has tried to assess or evaluate the CO2 emissions arising from household fuel use at homes, much attention with regard to energy use and the environment has been given to deforestation and other environmental problems resulting from the unsustainable consumption of fossil fuels. Besides, the environmental aspect of household energy use should be considered by focusing on CO2 emissions from commercial fuels (e.g., kerosene, LPG, diesel, petrol) used by urban households in meeting their energy needs.

Author Contributions

Resources, conceptualization, methodology, software, and writing—original draft preparation, O.R.; Funding acquisition, A.B.P. and S.R.; Validation, M.S., Y.G. and S.M.A.; Writing—review and editing, S.R., S.M.A. and B.O. All authors have read and agreed to the published version of the manuscript.


This research was partially supported by the University of Kurdistan Hewlêr (UKH).


The authors would like to appreciate The World Data, Reference [63], for providing the relevant information to reproduce Figure 3 and Figure 4. Figure 6 reprinted by permission (License Number: 4877490671536) from Elsevier: Journal of Cleaner Production, Reference [115], COPYRIGHT 2018. Figure 8 reprinted with permission (License Number: 4878380281534) from Elsevier: Energy, Reference [20], COPYRIGHT 2020.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Shittu, O. Emerging sustainability concerns and policy implications of urban household consumption: A systematic literature review. J. Clean. Prod. 2020, 246, 119034. [Google Scholar] [CrossRef]
  2. Rezaei, R.; Ghofranfarid, M. Rural households’ renewable energy usage intention in Iran: Extending the unified theory of acceptance and use of technology. Renew. Energy 2018, 122, 382–391. [Google Scholar] [CrossRef]
  3. Perry, K.K. For politics, people, or the planet? The political economy of fossil fuel reform, energy dependence and climate policy in Haiti. Energy Res. Soc. Sci. 2020, 63, 101397. [Google Scholar] [CrossRef]
  4. Yazdanpanah, M.; Komendantova, N.; Ardestani, R.S. Governance of energy transition in Iran: Investigating public acceptance and willingness to use renewable energy sources through socio-psychological model. Renew. Sustain. Energy Rev. 2015, 45, 565–573. [Google Scholar] [CrossRef]
  5. Woldeyohannes, A.D.; Woldemichael, D.E.; Baheta, A.T. Sustainable renewable energy resources utilization in rural areas. Renew. Sustain. Energy Rev. 2016, 66, 1–9. [Google Scholar] [CrossRef]
  6. Aryanpur, V.; Atabaki, M.S.; Marzband, M.; Siano, P.; Ghayoumi, K. An overview of energy planning in Iran and transition pathways towards sustainable electricity supply sector. Renew. Sustain. Energy Rev. 2019, 112, 58–74. [Google Scholar] [CrossRef]
  7. Papadis, E.; Tsatsaronis, G. Challenges in the decarbonization of the energy sector. Energy 2020, 205, 118025. [Google Scholar] [CrossRef]
  8. Druckman, A.; Jackson, T. Understanding Households as Drivers of Carbon Emissions. In Taking Stock of Industrial Ecology; Clift, R., Druckman, A., Eds.; Springer: Cham, Switzerland, 2016. [Google Scholar]
  9. Streimikiene, D.; Volochovic, A. The impact of household behavioral changes on GHG emission reduction in Lithuania. Renew. Sustain. Energy Rev. 2011, 15, 4118–4124. [Google Scholar] [CrossRef]
  10. Perera, F. Pollution from fossil-fuel combustion is the leading environmental threat to global pediatric health and equity: Solutions exist. Int. J. Environ. Res. Public Health 2018, 15, 16. [Google Scholar] [CrossRef] [Green Version]
  11. Oladokun, M.G.; Odesola, I.A. Household energy consumption and carbon emissions for sustainable cities–A critical review of modelling approaches. Int. J. Sustain. Built Environ. 2015, 4, 231–247. [Google Scholar] [CrossRef] [Green Version]
  12. Levine, M.; Ürge-Vorsatz, D.; Blok, K.; Geng, L.; Harvey, D.; Lang, S.; Levermore, G.; Mongameli Mehlwana, A.; Mirasgedis, S.; Novikova, A.; et al. Residential and commercial buildings. In Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Metz, B., Davidson, O.R., Bosch, P.R., Dave, R., Meyer, L.A., Eds.; Cambridge University Press: Cambridge, UK, 2007; Chapter 6; pp. 387–446. ISBN 9780521880091. [Google Scholar]
  13. Statistical Center of Iran (SCI). Selected Findings of the 2016 National Population and Housing Census. 2018. Available online: (accessed on 10 January 2019).
  14. The World Bank. Iran’s Economic Outlook. 2018. Available online: (accessed on 10 January 2019).
  15. The World Bank. 2019. Available online: (accessed on 12 June 2020).
  16. Mohammadnejad, M.; Ghazvini, M.; Mahlia, T.M.I.; Andriyana, A. A review on energy scenario and sustainable energy in Iran. Renew. Sustain. Energy Rev. 2011, 15, 4652–4658. [Google Scholar] [CrossRef]
  17. Lotfalipour, M.R.; Falahi, M.A.; Ashena, M. Economic growth, CO2 emissions, and fossil fuels consumption in Iran. Energy 2010, 35, 5115–5120. [Google Scholar] [CrossRef]
  18. Afsharzade, N.; Papzan, A.; Ashjaee, M.; Delangizan, S.; Van Passel, S.; Azadi, H. Renewable energy development in rural areas of Iran. Renew. Sustain. Energy Rev. 2016, 65, 743–755. [Google Scholar] [CrossRef] [Green Version]
  19. Soltani, M.; Rahmani, O.; Pour, A.B.; Ghaderpour, Y.; Ngah, I.; Misnan, S.H. Determinants of variation in household energy choice and consumption: Case from Mahabad City, Iran. Sustainability 2019, 11, 4775. [Google Scholar] [CrossRef] [Green Version]
  20. The World Bank. United Nations Population Division. World Urbanization Prospects: 2018 Revision. Available online: (accessed on 10 January 2019).
  21. Barkhordari, S.; Fattahi, M. Reform of energy prices, energy intensity and technology: A case study of Iran (ARDL approach). Energy Strateg. Rev. 2017, 18, 18–23. [Google Scholar] [CrossRef]
  22. World Economic Forum. 2019. Available online: (accessed on 12 June 2020).
  23. Fadai, D.; Esfandabadi, Z.S.; Abbasi, A. Analyzing the causes of non-development of renewable energy-related industries in Iran. Renew. Sustain. Energy Rev. 2011, 15, 2690–2695. [Google Scholar] [CrossRef]
  24. Alam, S.S.; Nik Hashim, N.H.; Rashid, M.; Omar, N.A.; Ahsan, N.; Ismail, M.D. Small-scale households renewable energy usage intention: Theoretical development and empirical settings. Renew. Energy 2014, 68, 255–263. [Google Scholar] [CrossRef]
  25. Bahrami, M.; Abbaszadeh, P. Development a scenario-based model for Iran’s energy future. Renew. Sustain. Energy Rev. 2016, 62, 963–970. [Google Scholar] [CrossRef]
  26. Fazelpour, F.; Markarian, E.; Soltani, N. Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran. Renew. Energy 2017, 109, 646–667. [Google Scholar] [CrossRef]
  27. Farajzadeh, Z.; Nematollahi, M.A. Energy intensity and its components in Iran: Determinants and trends. Energy Econ. 2018, 73, 161–177. [Google Scholar] [CrossRef]
  28. Soltani, M.; Rahmani, O.; Ghasimi, D.S.M.; Ghaderpour, Y.; Pour, A.B.; Misnan, S.H.; Ngah, I. Impact of household demographic characteristics on energy conservation and carbon dioxide emission: Case from Mahabad city, Iran. Energy 2020, 194, 116916. [Google Scholar] [CrossRef]
  29. Swan, L.G.; Ugursal, V.I. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renew. Sustain. Energy Rev. 2009, 13, 1819–1835. [Google Scholar] [CrossRef]
  30. Abrahamse, W.; Steg, L. Factors Related to Household Energy Use and Intention to Reduce It: The Role of Psychological and Socio-Demographic Variables. Hum. Ecol. Rev. 2011, 18, 30–40. [Google Scholar]
  31. Esen, Ö.; Bayrak, M. Does more energy consumption support economic growth in net energy-importing countries? J. Econ. Financ. Adm. Sci. 2017, 22, 75–98. [Google Scholar] [CrossRef]
  32. Zhao, R. Technology and economic growth: From Robert Solow to Paul Romer. Hum. Behav. Emerg. Technol. 2019, 1, 62–65. [Google Scholar] [CrossRef] [Green Version]
  33. Liu, Q.; Baumgartner, J.; de Foy, B.; Schauer, J.J. A global perspective on national climate mitigation priorities in the context of air pollution and sustainable development. City Environ. Interact. 2019, 1, 100003. [Google Scholar] [CrossRef]
  34. Batur, I.; Bayram, I.S.; Koc, M. Impact assessment of supply-side and demand-side policies on energy consumption and CO2 emissions from urban passenger transportation: The case of Istanbul. J. Clean. Prod. 2019, 219, 391–410. [Google Scholar] [CrossRef] [Green Version]
  35. Rodriguez, M.; Pansera, M.; Lorenzo, P.C. Do indicators have politics? A review of the use of energy and carbon intensity indicators in public debates. J. Clean. Prod. 2020, 243, 118602. [Google Scholar] [CrossRef]
  36. Moshiri, S. The effects of the energy price reform on households consumption in Iran. Energy Policy 2015, 79, 177–188. [Google Scholar] [CrossRef]
  37. Hosseini, S.M.; Saifoddin, A.; Shirmohammadi, R.; Aslani, A. Forecasting of CO2 emissions in Iran based on time series and regression analysis. Energy Rep. 2019, 5, 619–631. [Google Scholar] [CrossRef]
  38. UN-HABITAT. State of the world’s cities 2012/2013. Prosperity of Cities. United Nations Human Settlements Programme. 2012. Available online: (accessed on 12 June 2020).
  39. Ghorani-Azam, A.; Riahi-Zanjani, B.; Balali-Mood, M. Effects of air pollution on human health and practical measures for prevention in Iran. J. Res. Med. Sci. 2016, 21. [Google Scholar] [CrossRef]
  40. World Population Review. 2020. Available online: (accessed on 26 June 2020).
  41. Pazhuhan, M.; Shahraki, S.Z.; Kaveerad, N.; Cividino, S.; Clemente, M.; Salvati, L. Factors underlying life quality in urban contexts: Evidence from an industrial city (arak, iran). Sustainability 2020, 12, 2274. [Google Scholar] [CrossRef] [Green Version]
  42. Satterthwaite, D. The implications of population growth and urbanization for climate change. Environ. Urban. 2009, 21, 545–567. [Google Scholar] [CrossRef] [Green Version]
  43. UN Commission on Sustainable Development (UNCSD). Symposium on Sustainable Consumption; UNCSD: Oslo, Norway, 1994. [Google Scholar]
  44. Goggins, G.; Fahy, F.; Jensen, C.L. Sustainable transitions in residential energy use: Characteristics and governance of urban-based initiatives across Europe. J. Clean. Prod. 2019, 237, 117776. [Google Scholar] [CrossRef]
  45. Morone, P.; Falcone, P.M.; Lopolito, A. How to promote a new and sustainable food consumption model: A fuzzy cognitive map study. J. Clean. Prod. 2019, 208, 563–574. [Google Scholar] [CrossRef]
  46. Wolf, M.A.; Chomkhamsri, K. From Sustainable Production to Sustainable Consumption. In Life Cycle Management. LCA Compendium–The Complete World of Life Cycle Assessment; Sonnemann, G., Margni, M., Eds.; Springer: Dordrecht, The Netherlands, 2015. [Google Scholar] [CrossRef] [Green Version]
  47. Piligrimiene, Ž.; Žukauskaite, A.; Korzilius, H.; Banyte, J.; Dovaliene, A. Internal and external determinants of consumer engagement in sustainable consumption. Sustainability 2020, 12, 1349. [Google Scholar] [CrossRef] [Green Version]
  48. Moisander, J. Motivational complexity of green consumerism. Int. J. Consum. Stud. 2007, 31, 404–409. [Google Scholar] [CrossRef]
  49. Zukin, S.; Maguire, J.S. Consumers and Consumption. Annu. Rev. Sociol. 2004, 30, 173–197. [Google Scholar] [CrossRef] [Green Version]
  50. Warde, A. After taste: Culture, consumption and theories of practice. J. Consum. Cult. 2014, 14, 279–303. [Google Scholar] [CrossRef] [Green Version]
  51. Lorek, S.; Spangenberg, J.H. Environmentally Sustainable Houshold Consumption: From Aggregate Environmental Pressures to Indicators for Priority Fields of Action, Wuppertal Papers, No. 117, Wuppertal Institut für Klima, Umwelt, Energie, Wuppertal. 2001. Available online: (accessed on 26 June 2020).
  52. Wiggins, J. Motivation, Ability and Opportunity to Participate: A Reconceptualization of the RAND Model of Audience Development. Int. J. Arts Manag. 2004, 7, 22–33. [Google Scholar]
  53. Hung, K.; Sirakaya-Turk, E.; Ingram, L.J. Testing the Efficacy of an Integrative Model for Community Participation. J. Travel Res. 2011, 50, 276–288. [Google Scholar] [CrossRef]
  54. Gatersleben, B.; Vlek, C.A.J. Household consumption, quality of life and environmental impacts: A psychological perspective and empirical study. In Green Households: Domestic Consumers, the Environment and Sustainability; Noorman, K.J., Schoot-Uiterkamp, A.J.M., Eds.; Earthscan Publications: London, UK, 1998; pp. 141–183. [Google Scholar]
  55. Wu, C.; Zhou, X.; Song, M. Sustainable consumer behavior in China: An empirical analysis from the Midwest regions. J. Clean. Prod. 2016, 134, 147–165. [Google Scholar] [CrossRef]
  56. Zhu, Q.; Guo, Y. Statistical analysis on influencing factors and behavior of sustainable consumption. China Popul. Resour. Environ. 2011, 21, 459–463. (In Chinese) [Google Scholar]
  57. Burger, P.; Bezençon, V.; Bornemann, B.; Brosch, T.; Carabias-Hütter, V.; Farsi, M.; Hille, S.L.; Moser, C.; Ramseier, C.; Samuel, R.; et al. Advances in understanding energy consumption behavior and the governance of its change-Outline of an integrated framework. Front. Energy Res. 2015, 3. [Google Scholar] [CrossRef] [Green Version]
  58. Lipschutz, R. Practicing Energy, or Energy Consumption as Social Practice. In Proceedings of the Behavior, Energy and Climate Change Conference, Berkeley, CA, USA, 18–21 October 2015; Available online: (accessed on 12 June 2020).
  59. Kenny, T.; Gray, N.F. A preliminary survey of household and personal carbon dioxide emissions in Ireland. Environ. Int. 2009, 35, 259–272. [Google Scholar] [CrossRef] [PubMed]
  60. Sepehr, M.; Eghtedaei, R.; Toolabimoghadam, A.; Noorollahi, Y.; Mohammadi, M. Modeling the electrical energy consumption profile for residential buildings in Iran. Sustain. Cities Soc. 2018, 41, 481–489. [Google Scholar] [CrossRef]
  61. Khalili Araghi, M.; Barkhordari, S. An evaluation of the welfare effects of reducing energy subsides in Iran. Energy Policy 2012, 47, 398–404. [Google Scholar] [CrossRef]
  62. Nejat, P.; Jomehzadeh, F.; Taheri, M.M.; Gohari, M.; Muhd, M.Z. A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries). Renew. Sustain. Energy Rev. 2015, 43, 843–862. [Google Scholar] [CrossRef]
  63. World Data Atlas, Iran. 2019. Available online: (accessed on 26 June 2020).
  64. Hafeznia, H.; Pourfayaz, F.; Maleki, A. An assessment of Iran’s natural gas potential for transition toward low-carbon economy. Renew. Sustain. Energy Rev. 2017, 79, 71–81. [Google Scholar] [CrossRef]
  65. International Energy Agency (IEA). 2020. Available online: (accessed on 12 June 2020).
  66. Chavez, A.; Ramaswami, A. Progress toward low carbon cities: Approaches for transboundary GHG emissions’ footprinting. Carbon Manag. 2011, 2, 471–482. [Google Scholar] [CrossRef] [Green Version]
  67. Kawakubo, S.; Murakami, S.; Ikaga, T.; Asami, Y. Sustainability assessment of cities: SDGs and GHG emissions. Build. Res. Inf. 2018, 46, 528–539. [Google Scholar] [CrossRef]
  68. Song, M.; Zhao, X.; Shang, Y. The impact of low-carbon city construction on ecological efficiency: Empirical evidence from quasi-natural experiments. Resour. Conserv. Recycl. 2020, 157, 104777. [Google Scholar] [CrossRef]
  69. Hoornweg, D.; Sugar, L.; Gómez, C.L.T. Cities and greenhouse gas emissions: Moving forward. Environ. Urban. 2011, 23, 207–227. [Google Scholar] [CrossRef]
  70. Mondani, F.; Aleagha, S.; Khoramivafa, M.; Ghobadi, R. Evaluation of greenhouse gases emission based on energy consumption in wheat Agroecosystems. Energy Rep. 2017, 3, 37–45. [Google Scholar] [CrossRef]
  71. Rose, S.K.; Ahammad, H.; Eickhout, B.; Fisher, B.; Kurosawa, A.; Rao, S.; Riahi, K.; van Vuuren, D.P. Land-based mitigation in climate stabilization. Energy Econ. 2012, 34, 365–380. [Google Scholar] [CrossRef] [Green Version]
  72. Parvez, M.; Hazelton, J.; James, G. Greenhouse gas emissions disclosure by cities: The expectation gap. Sustain. Account. Manag. Policy J. 2019, 10, 685–709. [Google Scholar] [CrossRef]
  73. Wang, H.; Ang, B.W.; Zhou, P. Decomposing aggregate CO2 emission changes with heterogeneity: An extended production-theoretical approach. Energy J. 2018, 39, 59–79. [Google Scholar] [CrossRef]
  74. Qin, B.; Han, S. Spatial planning strategies for a low carbon city in China: Evidence from the neighborhoods of Beijing. Transform. Chin. Cities 2014, 231–247. [Google Scholar] [CrossRef]
  75. Qin, B.; Han, S.S. Planning parameters and household carbon emission: Evidence from high-and low-carbon neighborhoods in Beijing. Habitat Int. 2013, 37, 52–60. [Google Scholar] [CrossRef]
  76. Worldometer, Iran CO2Emissions. 2020. Available online: (accessed on 26 July 2020).
  77. Dubois, G.; Sovacool, B.; Aall, C.; Nilsson, M.; Barbier, C.; Herrmann, A.; Bruyère, S.; Andersson, C.; Skold, B.; Nadaud, F.; et al. It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures. Energy Res. Soc. Sci. 2019, 52, 144–158. [Google Scholar] [CrossRef]
  78. Steg, L.; Perlaviciute, G.; van der Werff, E. Understanding the human dimensions of a sustainable energy transition. Front. Psychol. 2015, 6, 805. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Orzan, G.; Cruceru, A.F.; Balaceanu, C.T.; Chivu, R.G. Consumers’ behavior concerning sustainable packaging: An exploratory study on Romanian consumers. Sustainability 2018, 10, 1787. [Google Scholar] [CrossRef]
  80. Saunders, H.D. The Khazzoom-Brookes Postulate and Neoclassical Growth. Energy J. 1992, 13, 130–148. [Google Scholar] [CrossRef]
  81. Walnum, H.J.; Aall, C.; Løkke, S. Can rebound effects explain why sustainable mobility has not been achieved? Sustainability 2014, 6, 9510–9537. [Google Scholar] [CrossRef] [Green Version]
  82. Wilhite, H. Will efficient technologies save the world? A call for new thinking on the ways that end-use technologies affect energy using practices. In Proceedings of the ECEEE 2007 Summer Study, Found a Future Energy Policy, La Colle sur Loup, Côte d’Azur, France, 4–9 June 2007; pp. 23–30. [Google Scholar]
  83. Greening, L.A.; Greene, D.L.; Difiglio, C. Energy efficiency and consumption—The rebound effect—A survey. Energy Policy 2000, 28, 389–401. [Google Scholar] [CrossRef]
  84. Reinders, A.H.M.E.; Vringer, K.; Blok, K. The direct and indirect energy requirement of households in the European Union. Energy Policy 2003, 31, 139–153. [Google Scholar] [CrossRef]
  85. He, Q.; Ng, S.T.; Hossain, M.U.; Skitmore, M. Energy-efficient window retrofit for high-rise residential buildings in different climatic zones of China. Sustainability 2019, 11, 6473. [Google Scholar] [CrossRef] [Green Version]
  86. Mulder, P.; de Groot, H.L.F. Structural change and convergence of energy intensity across OECD countries, 1970–2005. Energy Econ. 2012, 34, 1910–1921. [Google Scholar] [CrossRef] [Green Version]
  87. Sabetghadam, M. Energy and Sustainable Development in Iran. Helio International. 2006. Available online: (accessed on 12 June 2020).
  88. Rosas-Flores, J.A.; Gálvez, D.M. What goes up: Recent trends in Mexican residential energy use. Energy 2010, 35, 2596–2602. [Google Scholar] [CrossRef]
  89. Rosas-Flores, J.A.; Rosas-Flores, D.; Gálvez, D.M. Saturation, energy consumption, CO2 emission and energy efficiency from urban and rural households appliances in Mexico. Energy Build. 2011, 43, 10–18. [Google Scholar] [CrossRef]
  90. Rosas-Flores, J.A.; Zenón-Olvera, E.; Gálvez, D.M. Potential energy saving in urban and rural households of Mexico with solar photovoltaic systems using geographical information system. Renew. Sustain. Energy Rev. 2019, 116, 109412. [Google Scholar] [CrossRef]
  91. Barkhordar, Z.A. Evaluating the economy-wide effects of energy efficient lighting in the household sector of Iran. Energy Policy 2019, 127, 125–133. [Google Scholar] [CrossRef]
  92. Bin, S.; Dowlatabadi, H. Consumer lifestyle approach to US energy use and the related CO2 emissions. Energy Policy 2005, 33, 197–208. [Google Scholar] [CrossRef]
  93. Wei, Y.M.; Liu, L.C.; Fan, Y.; Wu, G. The impact of lifestyle on energy use and CO2 emission: An empirical analysis of China’s residents. Energy Policy 2007, 35, 247–257. [Google Scholar] [CrossRef]
  94. Yin, X.; Hao, Y.; Yang, Z.; Zhang, L.; Su, M.; Cheng, Y.; Zhang, P.; Yang, J.; Liang, S. Changing carbon footprint of urban household consumption in Beijing: Insight from a nested input-output analysis. J. Clean. Prod. 2020, 258. [Google Scholar] [CrossRef]
  95. Lee, C.C.; Ho, Y.M.; Chiu, H.Y. Role of personal conditions, housing properties, private loans, and housing tenure choice. Habitat Int. 2016, 53, 301–311. [Google Scholar] [CrossRef]
  96. De Almeida, A.; Fonseca, P.; Schlomann, B.; Feilberg, N. Characterization of the household electricity consumption in the EU, potential energy savings and specific policy recommendations. Energy Build. 2011, 43, 1884–1894. [Google Scholar] [CrossRef]
  97. Guerra-Santin, O.; Itard, L. The effect of energy performance regulations on energy consumption. Energy Effic. 2012, 5, 269–282. [Google Scholar] [CrossRef] [Green Version]
  98. Ürge-Vorsatz, D.; Cabeza, L.F.; Serrano, S.; Barreneche, C.; Petrichenko, K. Heating and cooling energy trends and drivers in buildings. Renew. Sustain. Energy Rev. 2015, 41, 85–98. [Google Scholar] [CrossRef] [Green Version]
  99. Satterthwaite, D.; McGranahan, G.; Tacoli, C. Urbanization and its implications for food and farming. Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 2809–2820. [Google Scholar] [CrossRef]
  100. Heinonen, J.; Junnila, S. Residential energy consumption patterns and the overall housing energy requirements of urban and rural households in Finland. Energy Build. 2014, 76, 295–303. [Google Scholar] [CrossRef]
  101. Norman, J.; MacLean, H.L.; Kennedy, C.A. Comparing high and low residential density: Life-cycle analysis of energy use and greenhouse gas emissions. J. Urban. Plan. Dev. 2006, 132, 10–21. [Google Scholar] [CrossRef]
  102. Ewing, R.; Rong, F. The impact of urban form on U.S. residential energy use. Hous. Policy Debate 2008, 19, 1–30. [Google Scholar] [CrossRef]
  103. Abrahamse, W.; Steg, L. How do socio-demographic and psychological factors relate to households’ direct and indirect energy use and savings? J. Econ. Psychol. 2009, 30, 711–720. [Google Scholar] [CrossRef]
  104. Frederiks, E.R.; Stenner, K.; Hobman, E.V. The socio-demographic and psychological predictors of residential energy consumption: A comprehensive review. Energies 2015, 8, 573–609. [Google Scholar] [CrossRef] [Green Version]
  105. Wilhite, H.; Nakagami, H.; Masuda, T.; Yamaga, Y.; Haneda, H. A cross-cultural analysis of household energy use behaviour in Japan and Norway. Energy Policy 1996, 24, 795–803. [Google Scholar] [CrossRef]
  106. Westrom, M. Bathing in Japan: Applying a practice theory vocabulary to energy use through ethnography. Energy Res. Soc. Sci. 2018, 44, 232–241. [Google Scholar] [CrossRef]
  107. Sovacool, B.K.; Griffiths, S. The cultural barriers to a low-carbon future: A review of six mobility and energy transitions across 28 countries. Renew. Sustain. Energy Rev. 2020, 119, 109569. [Google Scholar] [CrossRef]
  108. Lenzen, M.; Wier, M.; Cohen, C.; Hayami, H.; Pachauri, S.; Schaeffer, R. A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan. Energy 2006, 31, 181–207. [Google Scholar] [CrossRef]
  109. Park, H.C.; Heo, E. The direct and indirect household energy requirements in the Republic of Korea from 1980 to 2000-An input-output analysis. Energy Policy 2007, 35, 2839–2851. [Google Scholar] [CrossRef]
  110. Su, B.; Ang, B.W. Structural decomposition analysis applied to energy and emissions: Some methodological developments. Energy Econ. 2012, 34, 177–188. [Google Scholar] [CrossRef]
  111. Zhang, L.; Hu, Q.; Zhang, F. Input-output modeling for urban energy consumption in Beijing: Dynamics and comparison. PLoS ONE 2014, 9, e89850. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  112. Choi, J.K.; Bakshi, B.R.; Hubacek, K.; Nader, J. A sequential input–output framework to analyze the economic and environmental implications of energy policies: Gas taxes and fuel subsidies. Appl. Energy 2016, 184, 830–839. [Google Scholar] [CrossRef] [Green Version]
  113. Supasa, T.; Hsiau, S.S.; Lin, S.M.; Wongsapai, W.; Wu, J.C. Household energy consumption behaviour for different demographic regions in Thailand from 2000 to 2010. Sustainability 2017, 9, 2328. [Google Scholar] [CrossRef] [Green Version]
  114. Kok, R.; Benders, R.M.J.; Moll, H.C. Measuring the environmental load of household consumption using some methods based on input-output energy analysis: A comparison of methods and a discussion of results. Energy Policy 2006, 34, 2744–2761. [Google Scholar] [CrossRef]
  115. Hajilary, N.; Shahi, A.; Rezakazemi, M. Evaluation of socio-economic factors on CO2 emissions in Iran: Factorial design and multivariable methods. J. Clean. Prod. 2018, 189, 108–115. [Google Scholar] [CrossRef]
  116. Pachauri, S. An analysis of cross-sectional variations in total household energy requirements in India using micro survey data. Energy Policy 2004, 32, 1723–1735. [Google Scholar] [CrossRef]
  117. Muñoz, P.; Zwick, S.; Mirzabaev, A. The impact of urbanization on Austria’s carbon footprint. J. Clean. Prod. 2020, 263, 121326. [Google Scholar] [CrossRef]
  118. Narasimha Rao, M.; Reddy, B.S. Variations in energy use by Indian households: An analysis of micro level data. Energy 2007, 32, 143–153. [Google Scholar] [CrossRef]
  119. Gould, C.F.; Urpelainen, J.; Hopkins SAIS, J. The role of education and attitudes in cooking fuel choice: Evidence from two states in India. Energy Sustain. Dev. 2020, 54, 36–50. [Google Scholar] [CrossRef]
  120. O’Neill, B.C.; Belinda, S.C. Demographic Determinants of Household Energy Use in the United States. Popul. Dev. Rev. 2002, 28, 53–88. Available online: (accessed on 6 June 2020).
  121. Sadati, S.; Edwards, R. Incorporating solar energy sources in low energy buildings in two major cities in Iran. Energy Procedia 2019, 156, 85–89. [Google Scholar] [CrossRef]
  122. Zaharia, A.; Diaconeasa, M.C.; Brad, L.; Lădaru, G.R.; Ioanăs, C. Factors influencing energy consumption in the context of sustainable development. Sustainability 2019, 11, 4147. [Google Scholar] [CrossRef] [Green Version]
  123. Muller, C.; Yan, H. Household fuel use in developing countries: Review of theory and evidence. Energy Econ. 2018, 70, 429–439. [Google Scholar] [CrossRef]
  124. World Health Organization (WHO). Fuel for Life: Household Energy and Health; WHO Library: Geneva, Switzerland, 2006; p. 23. [Google Scholar]
  125. Twumasi, M.A.; Jiang, Y.; Ameyaw, B.; Danquah, F.O.; Acheampong, M.O. The impact of credit accessibility on rural households clean cooking energy consumption: The case of Ghana. Energy Rep. 2020, 6, 974–983. [Google Scholar] [CrossRef]
  126. Ma, W.; Zhou, X.; Renwick, A. Impact of off-farm income on household energy expenditures in China: Implications for rural energy transition. Energy Policy 2019, 127, 248–258. [Google Scholar] [CrossRef]
  127. Heltberg, R. Factors determining household fuel choice in Guatemala. Environ. Dev. Econ. 2005, 10, 337–361. [Google Scholar] [CrossRef]
  128. Modi, V.; McDade, S.; Lallement, D.; Saghir, J. Energy Services for the Millennium Development Goals; The International Bank for Reconstruction and Development/The World Bank/ESMAP: Washington, DC, USA, 2005; Available online: (accessed on 6 June 2020).
  129. Sovacool, B.K. Conceptualizing urban household energy use: Climbing the “Energy Services Ladder”. Energy Policy 2011, 39, 1659–1668. [Google Scholar] [CrossRef]
  130. Haas, R.; Nakicenovic, N.; Ajanovic, A.; Faber, T.; Kranzl, L.; Müller, A.; Resch, G. Towards sustainability of energy systems: A primer on how to apply the concept of energy services to identify necessary trends and policies. Energy Policy 2008, 36, 4012–4021. [Google Scholar] [CrossRef]
  131. Sorrell, S.; Gatersleben, B.; Druckman, A. The limits of energy sufficiency: A review of the evidence for rebound effects and negative spillovers from behavioural change. Energy Res. Soc. Sci. 2020, 64, 101439. [Google Scholar] [CrossRef]
  132. Barnes, D.F.; Floor, W.M. Rural energy in developing countries: A challenge for economic development. Annu. Rev. Energy Environ. 1996, 21, 497–530. [Google Scholar] [CrossRef]
  133. Dutschke, M.; Kapp, G.; Lehmann, A.; Sch, V.; Economics, I. Risks and Chances of Combined Forestry and Biomass Projects under the Clean Development Mechanism. Sustain. Dev. 2006, 121, 351–397. [Google Scholar]
  134. Rena, R. Renewable Energy for Rural Development–A Namibian Experience. In Rural Development-Contemporary Issues and Practices; IntechOpen: London, UK, 2012; Available online: (accessed on 14 May 2020).
  135. Fouquet, R. Heat, Power and Light: Revolutions in Energy Services; Edward Elgar: Cheltenham, UK, 2009; ISBN 9781845426606. [Google Scholar]
  136. Loveday, D.L.; Bhamra, T.; Tang, T.; Haines, V.J.A.; Holmes, M.J.; Green, R.J. The energy and monetary implications of the “24/7” “always on” society. Energy Policy 2008, 36, 4639–4645. [Google Scholar] [CrossRef]
  137. Wilk, R.R. Culture and Energy Consumption. Energy Sci. Policy Purs. Sustain. 2002, 1, 109–130. [Google Scholar]
  138. Joon, V.; Chandra, A.; Bhattacharya, M. Household energy consumption pattern and socio-cultural dimensions associated with it: A case study of rural Haryana, India. Biomass Bioenergy 2009, 33, 1509–1512. [Google Scholar] [CrossRef]
  139. Le, V.T.; Pitts, A. A survey on electrical appliance use and energy consumption in Vietnamese households: Case study of Tuy Hoa city. Energy Build. 2019, 197, 229–241. [Google Scholar] [CrossRef]
  140. Barnes, D.F.; Krutilla, K.; Hyde, W. The Urban Household Energy Transition; Routledge: New York, NY, USA, 2005. [Google Scholar] [CrossRef]
  141. García-Valladares, O.; Ituna-Yudonago, J.F. Energy, economic and emissions avoided contribution of domestic solar water heating systems for Mexico, Costa Rica and the Democratic Republic of the Congo. Sustain. Energy Technol. Assess. 2020, 39, 100721. [Google Scholar] [CrossRef]
  142. Nansaior, A.; Patanothai, A.; Rambo, A.T.; Simaraks, S. Climbing the energy ladder or diversifying energy sources? The continuing importance of household use of biomass energy in urbanizing communities in Northeast Thailand. Biomass Bioenergy 2011, 35, 4180–4188. [Google Scholar] [CrossRef]
  143. Chambwera, M. Economic Analysis of Urban Fuelwood Demand: The Case of Harare in Zimbabwe; Wageningen Universiteit: Wageningen, The Netherlands, 2004. [Google Scholar]
  144. Mekonnen, A.; Gunnar, K. Determinants of household fuel choice in major cities in Ethiopia. In Environment for Development Initiative; Environment for Development: Gothenburg, Sweden, 2008; Available online: (accessed on 14 May 2020).
  145. Gebreegziabher, Z.; Mekonnen, A.; Kassie, M.; Köhlin, G. Urban energy transition and technology adoption: The case of Tigrai, northern Ethiopia. Energy Econ. 2012, 34, 410–418. [Google Scholar] [CrossRef]
  146. Brizga, J.; Feng, K.; Hubacek, K. Household carbon footprints in the Baltic States: A global multi-regional input–output analysis from 1995 to 2011. Appl. Energy 2017, 189, 780–788. [Google Scholar] [CrossRef]
  147. Dai, H.; Masui, T.; Matsuoka, Y.; Fujimori, S. The impacts of China’s household consumption expenditure patterns on energy demand and carbon emissions towards 2050. Energy Policy 2012, 50, 736–750. [Google Scholar] [CrossRef]
  148. Zhang, J.; Yu, B.; Wei, Y.M. Heterogeneous impacts of households on carbon dioxide emissions in Chinese provinces. Appl. Energy 2018, 229, 236–252. [Google Scholar] [CrossRef]
  149. Barr, S.; Gilg, A.W.; Ford, N. The household energy gap: Examining the divide between habitual- and purchase-related conservation behaviours. Energy Policy 2005, 33, 1425–1444. [Google Scholar] [CrossRef]
  150. Trotta, G. Factors affecting energy-saving behaviours and energy efficiency investments in British households. Energy Policy 2018, 114, 529–539. [Google Scholar] [CrossRef]
  151. Fuerst, F.; Kavarnou, D.; Singh, R.; Adan, H. Determinants of energy consumption and exposure to energy price risk: A UK studyDeterminanten des Energieverbrauchs und Energiepreisrisiko: Eine Studie aus Großbritannien. Z. Für Immob. 2020, 6, 65–80. [Google Scholar] [CrossRef]
  152. Lenzen, M.; Kanemoto, K.; Moran, D.; Geschke, A. Mapping the structure of the world economy. Environ. Sci. Technol. 2012, 46, 8374–8381. [Google Scholar] [CrossRef]
  153. Barrett, J.; Peters, G.; Wiedmann, T.; Scott, K.; Lenzen, M.; Roelich, K.; Le Quéré, C. Consumption-based GHG emission accounting: A UK case study. Clim. Policy 2013, 13, 451–470. [Google Scholar] [CrossRef]
  154. Sorrell, S. Reducing energy demand: A review of issues, challenges and approaches. Renew. Sustain. Energy Rev. 2015, 47, 74–82. [Google Scholar] [CrossRef] [Green Version]
  155. IEA World Energy Outlook. 2018. Available online: (accessed on 26 July 2020).
  156. Moll, H.C.; Noorman, K.J.; Kok, R.; Engström, R.; Throne-Holst, H.; Clark, C. Pursuing more sustainable consumption by analyzing household metabolism in European countries and cities. Proc. J. Ind. Ecol. 2005, 9, 259–275. [Google Scholar] [CrossRef] [Green Version]
  157. Bhati, A.; Hansen, M.; Chan, C.M. Energy conservation through smart homes in a smart city: A lesson for Singapore households. Energy Policy 2017, 104, 230–239. [Google Scholar] [CrossRef]
  158. Cayla, J.M.; Maizi, N.; Marchand, C. The role of income in energy consumption behaviour: Evidence from French households data. Energy Policy 2011, 39, 7874–7883. [Google Scholar] [CrossRef]
  159. Leth-Petersen, S.; Togeby, M. Demand for space heating in apartment blocks: Measuring effects of policy measures aiming at reducing energy consumption. Energy Econ. 2001, 23, 387–403. [Google Scholar] [CrossRef]
  160. Lazowski, B.; Parker, P.; Rowlands, I.H. Towards a smart and sustainable residential energy culture: Assessing participant feedback from a long-term smart grid pilot project. Energy Sustain. Soc. 2018, 8, 27. [Google Scholar] [CrossRef] [Green Version]
  161. Glaeser, E.L.; Kahn, M.E. The greenness of cities: Carbon dioxide emissions and urban development. J. Urban. Econ. 2010, 67, 404–418. [Google Scholar] [CrossRef] [Green Version]
  162. Cheng, X.; Li, S. Interval estimations of building heating energy consumption using the degree-day method and fuzzy numbers. Buildings 2018, 8, 21. [Google Scholar] [CrossRef] [Green Version]
  163. Zhang, Q.; Zhuang, S.; Yang, H. Comparison of Residential Energy Consumption in China, Japan, Canada and USA. J. Asian Archit. Build. Eng. 2003, 2, 101–106. [Google Scholar] [CrossRef] [Green Version]
  164. Sarak, H.; Satman, A. The degree-day method to estimate the residential heating natural gas consumption in Turkey: A case study. Energy 2003, 28, 929–939. [Google Scholar] [CrossRef]
  165. Kohler, M.; Blond, N.; Clappier, A. A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France). Appl. Energy 2016, 184, 40–54. [Google Scholar] [CrossRef]
  166. Yun, G.Y.; Steemers, K. Behavioural, physical and socio-economic factors in household cooling energy consumption. Appl. Energy 2011, 88, 2191–2200. [Google Scholar] [CrossRef]
  167. Martinsson, J.; Lundqvist, L.J.; Sundström, A. Energy saving in Swedish households. The (relative) importance of environmental attitudes. Energy Policy 2011, 39, 5182–5191. [Google Scholar] [CrossRef]
  168. Wiesmann, D.; Lima Azevedo, I.; Ferrão, P.; Fernández, J.E. Residential electricity consumption in Portugal: Findings from top-down and bottom-up models. Energy Policy 2011, 39, 2772–2779. [Google Scholar] [CrossRef]
  169. Van Der Kroon, B.; Brouwer, R.; Van Beukering, P.J.H. The energy ladder: Theoretical myth or empirical truth? Results from a meta-analysis. Renew. Sustain. Energy Rev. 2013, 20, 504–513. [Google Scholar] [CrossRef]
  170. Ruiz-Mercado, I.; Masera, O. Patterns of Stove Use in the Context of Fuel–Device Stacking: Rationale and Implications. Ecohealth 2015, 12, 42–56. [Google Scholar] [CrossRef]
  171. Choumert-Nkolo, J.; Combes Motel, P.; Le Roux, L. Stacking up the ladder: A panel data analysis of Tanzanian household energy choices. World Dev. 2019, 115, 222–235. [Google Scholar] [CrossRef]
  172. IEA Energy Consumption. 2018. Available online: (accessed on 14 May 2020).
  173. Moshiri, S.; Atabi, F.; Panjehshahi, M.H.; Lechtenböehmer, S. Long run energy demand in Iran: A scenario analysis. Int. J. Energy Sect. Manag. 2012, 6, 120–144. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Main countries responsible for global CO2 emissions, modified from [22].
Figure 1. Main countries responsible for global CO2 emissions, modified from [22].
Processes 08 00994 g001
Figure 2. The needs opportunities ability (NOA) model of consumer behavior, modified from [54].
Figure 2. The needs opportunities ability (NOA) model of consumer behavior, modified from [54].
Processes 08 00994 g002
Figure 3. Sectorial CO2 emissions in Iran from 1971 to 2018, modified from [63].
Figure 3. Sectorial CO2 emissions in Iran from 1971 to 2018, modified from [63].
Processes 08 00994 g003
Figure 4. Per capita CO2 emissions in Iran, modified from [63].
Figure 4. Per capita CO2 emissions in Iran, modified from [63].
Processes 08 00994 g004
Figure 5. Fossil CO2 emissions by different sectors in Iran, modified from [76].
Figure 5. Fossil CO2 emissions by different sectors in Iran, modified from [76].
Processes 08 00994 g005
Figure 6. Distribution of energy consumption in different sections in Iran [115]. Reproduced with copyright, Elsevier.
Figure 6. Distribution of energy consumption in different sections in Iran [115]. Reproduced with copyright, Elsevier.
Processes 08 00994 g006
Figure 7. Energy ladder model, modified from the WHO [124].
Figure 7. Energy ladder model, modified from the WHO [124].
Processes 08 00994 g007
Figure 8. Influential factors on the CO2 emissions from Iranian households energy consumption [28]. Reproduced with copyright, Elsevier.
Figure 8. Influential factors on the CO2 emissions from Iranian households energy consumption [28]. Reproduced with copyright, Elsevier.
Processes 08 00994 g008
Figure 9. Specific energy consumption in terms of electricity based in Iran from 1999 to 2017, modified from [172]. Terawatt-hour (TW h), a measure of electrical energy, equals to 1012 watt-hours.
Figure 9. Specific energy consumption in terms of electricity based in Iran from 1999 to 2017, modified from [172]. Terawatt-hour (TW h), a measure of electrical energy, equals to 1012 watt-hours.
Processes 08 00994 g009

Share and Cite

MDPI and ACS Style

Rahmani, O.; Rezania, S.; Beiranvand Pour, A.; Aminpour, S.M.; Soltani, M.; Ghaderpour, Y.; Oryani, B. An Overview of Household Energy Consumption and Carbon Dioxide Emissions in Iran. Processes 2020, 8, 994.

AMA Style

Rahmani O, Rezania S, Beiranvand Pour A, Aminpour SM, Soltani M, Ghaderpour Y, Oryani B. An Overview of Household Energy Consumption and Carbon Dioxide Emissions in Iran. Processes. 2020; 8(8):994.

Chicago/Turabian Style

Rahmani, Omeid, Shahabaldin Rezania, Amin Beiranvand Pour, Shahram M. Aminpour, Mohammad Soltani, Yousef Ghaderpour, and Bahareh Oryani. 2020. "An Overview of Household Energy Consumption and Carbon Dioxide Emissions in Iran" Processes 8, no. 8: 994.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop