1. Introduction
The IPCC 1.5-Degree C special report [
1] highlights that global warming of 2 °C would have a significantly larger risk than 1.5 °C. Rapid and far-reaching low-carbon actions are required to limit warming to 1.5 °C. According to the record from Energy and Climate Intelligence Unit [
2], more than 130 countries or regions have proposed carbon neutral goals, of which 17 countries have enshrined the target in law and over 30 countries included their climate target in policy documents. Although achieving the carbon neutral goal is a global challenge, it is also a powerful driver for the energy revolution to achieve sustainable development.
As both the largest developing country and the largest CO
2 emitter in the world, China is playing a critical role in achieving the global climate goal. In this context, China has made great efforts in controlling energy-related CO
2 emissions and moving to a cleaner and more efficient economy [
3]. In 2014, China pledged to achieve the peaking of CO
2 emissions around 2030 and to try to peak it earlier in a joint US–China government announcement [
4]. The goal was reaffirmed, along with an additional target to reduce CO
2 intensity by 60–65% by 2030, below 2005 levels, as part of the Enhanced Action of Climate Change: China’s Intended Nationally Determined Contributions (INDCs) [
5]. Most recently, at the 75th United Nations General Assembly in September 2020, the Chinese government announced that China will aim to achieve carbon neutrality before 2060, which is a big shift for curbing emissions and a significant step forward in international cooperation. Indeed, China has achieved remarkable results in combating climate change. According to the recent report on China’s policies and actions for addressing climate change in 2018, China had achieved its 2020 carbon emission target three years ahead of schedule, which was reversing the rapidly rising trend of CO
2 emissions [
6].
Various models have been developed to understand China’s future energy and emission trend, mainly including the top-down or general equilibrium models, the bottom-up models, and hybrid models [
7,
8]. Zhou et al. [
9] presented a comprehensive review of recent China long-term energy and emissions modelling efforts. It has been reported that, from 2005 to 2013, over eighteen modelling tools have been developed to study China’s future emissions [
10]. Jiang et al. [
11] presented a critical review regarding sectoral and provincial CO
2 emission in China and indicated that an in-depth examination should be conducted in key provinces.
Because of the notable provincial discrepancies in energy-source mixes, geological conditions, population level, socioeconomic development, etc., in practicality, China’s peak emissions targets are expected to be designed for the sub-administrative region such that the national total peaks by 2030 [
12,
13]. Therefore, a better understanding of regional peak CO
2 emission targets and the establishment of a feature-based emission roadmap is fundamental for determining the energy and carbon emission profile of the nation, and extremely important for achieving national goals [
14,
15]. At present, more than 10 provinces (municipalities) in China have proposed their peak targets.
A number of studies have been conducted on CO
2 or greenhouse gas (GHG) peaking in China at the regional level, as summarized in
Table 1. At the provincial level, Yophy et al. [
16] and Tian et al. [
17] forecasted the CO
2 emissions of Taiwan and Jilin [
18] using the Low Emissions Analysis Platform (LEAP) model combined with scenario analysis. Several studies adopted the STIRPAT model to analyze the influence factors of carbon emissions from a macroscopic view. Wen and Liu [
19], Zhao et al. [
20], and Qin [
21] employed the STIRPAT model to analyze the CO
2 emissions for Beijing–Tianjin–Hebei, Henan province, and Xinjiang Autonomous Region, respectively. From a top-down perspective, Feng and Wang [
22], used the input-output model of Structural Decomposition Analysis (SDA) to decompose the carbon emissions of Shanxi Province. Specifically, Zhang et al. integrated the STIRPAT and LEAP model to achieve crosschecks of emission peaks and mitigation roadmaps of Yunnan Province. The results from the combining work indicate that the peak emissions will occur over the prolonged period from 2024 to 2028.
Several studies have focused on the energy consumption, emissions peak, strategies, and the contributing factors at the city level. Feng et al. [
15] developed an integrated system dynamics model to predict the energy and CO
2 profiles for Beijing city. Lin et al. [
7] applied a LEAP-based model to study the CO
2 and GHG peak volume and time by integrating energy-related and non-energy-related sectors of Xiamen city. Using the hybrid method, the long-mean divisia index (LMDI) method was coupled with STRPAT [
23], the BP neural network model [
24] and system dynamic [
18] to decompose the determinations of urban CO
2 change and predict emission peaks for Chongqing, Shanghai and Baoding cities.
Table 1.
Summary of research on Chinese CO2 emissions peak at the regional level.
Table 1.
Summary of research on Chinese CO2 emissions peak at the regional level.
Study | Possible Peak Time | Research Model | Study Method | Scenario Analysis | Ref. |
---|
Provincial Level |
Taiwan | - | Bottom-up | LEAP | √ | [16] |
Shanxi | 2029–2030 | Top-down | IO-SDA | √ | [22] |
Jilin | 2025–2045 | Bottom-up | LEAP | √ | [17] |
Henan | 2035–2040 | Top-down | STIRPAT | √ | [20] |
Beijing-Tianjin-Hebei | 2029–2045 | Top-down | STIRPAT | √ | [19] |
Yunnan | 2024–2028 | Top-down | STIRPAT | - | [14] |
Bottom-up | LEAP | √ |
Xinjiang | After 2030 | Top-down | STIRPAT | √ | [21] |
City Level |
Beijing | – | System dynamics model | Beijing-STELLA | - | [15] |
Beijing | – | Bottom-up | LEAP | √ | [25] |
Beijing | 2019 | Top-down | Kaya | - | [26] |
Kunming | 2021–2028 | Top-down | STIRPAT | √ | [27] |
Guangzhou | 2020 | Bottom-up | Scenario analysis | √ | [28] |
Chongqing | 2032–2035 | Hybrid | LMDI and STRPAT | √ | [23] |
Qingdao | 2020–2025 | Top-down | STIRPAT | √ | [29] |
Xiamen | 2034–2039 a | Bottom-up | LEAP | √ | [7] |
Baoding | 2024 | Hybrid | LMDI and BP neural network model | √ | [24] |
Shanghai | 2025 | Hybrid | LMDI and System dynamic model | √ | [18] |
As the largest economy and the most populous province in China, the economic and social development stage of Guangdong is typical and representative in the country. Promoting Guangdong’s early achievement of peak carbon emissions will not only accelerate its high-quality economic development but also help accumulate typical experience for China to explore provincial energy-saving and emission-reduction paths and measures. Several studies have been conducted focusing on CO
2 accounting, peak prediction as well as electricity and carbon market of Guangdong Province. Wang et al. [
30] examined the impact factors of energy-related CO
2 emissions using an extended STIPRAT model. The results indicate that population has the strongest influence on CO
2 increase. Based on the IPCC territorial emission accounting approach, Zhou et al. [
31] firstly complies with emissions inventories of eleven Guangdong–Hong Kong–Macao Greater Bay areas and their surroundings from the year 2000 to 2016. The key emission contributors and characteristics were identified and discussed for different cities. Jiang et al. [
32] assessed the economic and CO
2 emissions impacts of electricity market reforms in Guangdong Province. The results indicate that relatively high CO
2 prices at around 260 CNY/tCO
2 would be necessary to avoid emission increases during the market transition. Li and Li [
33] developed a multi-scenario ensemble simulation and environmental input-output (MES-EIO) to identify the optimal multi-pollutant and emissions reduction schemes from 2020 to 2030 for Guangdong Province. Integrating the EIO and RAS model, Feng and Bai [
34] predicted the energy demand and related CO
2 emissions of Guangdong Province. The results indicate that with the decline in economic growth, the speed of energy efficiency improvement is one of the most critical factors determining the peak time for Guangdong Province.
Compared with a large number of research results on energy demand forecasting at the national level, there are fewer forecasting studies on energy demand and carbon emissions in Guangdong Province. Since the 13th Five-Year Plan, the Guangdong government and local authorities have implemented a series of low-carbon policies. However, previous research rarely considered the local policies. Specifically, the overall layout plant for 5G base stations and data centers for Guangdong Province was released recently. With the large-scale use of data centers and 5G bases stations, the energy consumption per unit of value-added and total energy consumption may continue to increase, which may bring a new challenge for the CO2 peak in Guangdong. Therefore, both low-carbon and energy policies will significantly affect carbon emissions and peak values. However, fewer studies were conducted to predict the CO2 emissions and quantify the peak values under the current policy environment, which could provide data support for the development of a local low-carbon economy.
The contributions of this study are mainly embodied in the following aspect. In terms of research content, the characteristics of Guangdong’s energy-related CO2 emissions are analyzed based on a developed bottom-up energy model. Additionally, based on the policies that are already being implemented or policies that appear certain to be implemented in the short to medium term, the CO2 emissions trends and reduction potentials are forecasted in three macro-economic scenarios at different improvement levels in energy intensity and structure through scenario analysis. The likelihood of meeting the carbon peak goal for Guangdong Province is examined, and practical policy recommendations are proposed. The research results can provide insights into Guangdong’s CO2 peak as well as develop strategies for carbon emission reduction.
The flowchart of the methodology of this study is shown in
Figure 1. Based on this framework,
Section 2 introduces the historical data and related policies and strategies of Guangdong Province. On the bases of the historical trends and driving forces, in
Section 3 multiple scenarios were proposed and the framework of the end-use energy-base emission model was introduced.
Section 4 and
Section 5 present a depth analysis of the energy-related CO
2 trajectories of Guangdong Province and corresponding policy implications.
6. Conclusions
As the largest economy and the first batch of the low-carbon pilot province, the research on carbon emission peak in Guangdong Province is of great importance for achieving national reduction goals in China. This study thus investigated Guangdong’s energy-related CO2 emissions with scenario analysis based on the latest data and policy, which can help provide an understanding of the regional CO2 peak in China and help Guangdong to develop a carbon peaking action plan.
Based on the CO2 emission analysis, Guangdong Province cannot reach the emission peak before 2030 by doing nothing, even under low economic growth rate scenarios. Optimization of energy structure, or controlling the share of coal consumption, is critical to achieving the carbon peak in Guangdong. The energy-related CO2 emissions will reach a plateau between 2025 and 2027 by implementing the medium and strengthening optimization of energy structure under medium and slow economic scenarios, with a CO2 peak value between 0.61 and 0.64 Gt.
Sector-wise, the peak of CO2 emissions will keep pace with the CO2 peak in industrial sectors under medium energy intensity and structure scenarios. In addition, CO2 from transportation and other tertiary industrial sectors will also experience a decreasing trend after 2030. Nevertheless, since Guangdong Province is still experiencing rapid growth in population growth, the CO2 increase in residential sectors needs to be balanced by CO2 mitigation in the industry and transportation sectors. Meanwhile, CO2 emissions from coal consumption will still dominate the total CO2 trend throughout the research period. Thus, controlling coal consumption is the key to reaching the CO2 peak goal.
Overall, to achieve Guangdong’s peak target for energy-related CO2 emissions, effective policies are required for both energy efficiency improvement and controlling the increase in coal consumption. The total energy consumption is suggested to be within 460 million standard coal by 2030, of which coal consumption accounts for no more than 22%. The following comprehensive supportive measures are required. The first is to vigorously develop non-fossil energy, especially for offshore wind and nuclear power to meet over 90% power increments in 2030. Second, increase the scale of imported green electricity. Third, accelerate the development of high-tech industries and take actions on low-carbon development for transportation and building sectors. Lastly, strengthening the policy measures on projects including coal-to-gas conversion in the industrial sector, the green hydrogen energy industry, carbon capture, utilization and storage, zero-carbon demonstration, green finance, and the green supply chain is required. Further study is recommended to analyze the CO2 and non-CO2 GHG emissions from the whole society by implementing both top-down and bottom-up methods, and paying more attention to cost, technical and environmental trade-offs, and constraints for the large deployment of low-carbon technologies.