The Measurements and Analysis of Spatial-Temporal Variations of Human Development Index Based on Planetary Boundaries in China: Evidence from Provincial-Level Data

Abstract

It is necessary to pursue the economic and social development of humanity to cope with the challenges of the global ecological environment within the constraints of planetary boundaries. For constructing the evaluation model of human development considering the earth pressure from the sub-national level, and observing the changes of human development level under the earth pressure in China in recent years, this paper constructs the PB-HDI (Planetary Boundaries-Human Development Index) index to measure the human development level under planetary boundaries in 30 Chinese provinces of 2010, 2014, 2017, and 2020; and carries out the analysis of evolutionary characteristics and spatial heterogeneity inspired by the path of balancing the relationship between environmental protection and economic development in China. We found: (1) the regional PB-HDI differences show a convergence trend, with a gradual decrease in low-level provinces; (2) the regional heterogeneity of PB-HDI is obvious. The differences between eastern provinces are the largest; (3) resources and environment constitute the outer circle of economic and social development, forming a “doughnut” inclusion pattern that discourages high-level development beyond the boundary and low-level development within the boundary. In general, there are significant differences in economic development, environmental protection level, social security capacity, industrial structure, innovation level, policy environment, and other basic conditions among different regions of China, and sustainable development paths need to be determined according to local conditions. This study is critical for expanding the application of the sub-national human development assessment for global stress and optimizing China’s sustainable development path.

1. Introduction

In recent years, resources shortage, biodiversity loss, increasing desertification, frequent extreme weather events, and severe environmental pollution have posed great challenges to human survival and development globally (Chou et al., 2018 [1]). People have gradually realized that the increasingly strong coupling relationship between society and the ecosystem (Fischer et al., 2015 [2]). And sustainable development is imminent since resources and the environment have become significant constraints on economic and social development. It needs to be clear that human activities should be within the carrying capacity of the Earth’s resources and environment. Human activities often have a greater impact on sustainable development than we can imagine (Steffen et al., 2018 [3]). In the context of globalization, no country or people can be independent. For example, the Russian-Ukrainian war directly leads to huge damage to the ecological environment which affects human health and the stability of the ecosystem. And the healthy development of the economy is hindered because the energy and food markets are impacted (Pereira et al., 2022 [4]).

Therefore, it is fundamental to further deepen the understanding of the relationship between the environment and human development. This requires a shift in the sustainable development paradigm from a weak sustainable development paradigm in which economic, social, and environmental aspects are juxtaposed to a strong sustainable development paradigm in which environmental, social, and economic aspects are sequentially included. This means that we should be concerned about changes in human well-being within the ecological carrying capacity. The shift in mindset means that the accompanying assessment system needs to be upgraded. Scholars have attempted to move from a single focus on environmental, social, or economic sustainability assessment to an integrated assessment system. For example, the evolution from resource carrying capacity and GDP to the evaluation systems that include multiple social and economic elements such as green GDP, HDI, SDGs, and eco-welfare performance (Giannetti et al., 2015 [5]). How to combine the assessments of the environment, economy, and society has become the focus of scholars’ research work. Especially the environmental system is complex, with highly interconnected subsystems that are not easy to measure. There is an urgent need for innovative exploration of theory and practice to enrich and expand sustainable development assessment work.

The proposal of planetary boundaries provides a new perspective to address the dilemma of sustainable development. The planetary boundaries framework quantifies human-caused environmental changes that may destabilize the long-term dynamics of the Earth system, which could help humanity prevent unacceptable environmental changes from human activities. And the framework holds that crossing planetary boundaries may significantly reduce the possibility of humans maintaining a Holocene-like state in the Anthropocene (Rockström et al., 2009 [6]; Steffen et al., 2015 [7]). The planetary boundaries framework has been widely discussed by scholars, the public, and policymakers around the world, and has been promoted and applied in the field of sustainable development, such as the report “The Road to Dignity by 2030: Ending Poverty, Transforming All Lives and Protecting the Planet”, the EU’s 7th Environmental Action Programme “Living Well, within the Limits of Our Planet”, and the Human Development Report 2020. The pursuit of continuous improvement of human well-being under the planetary boundaries constraint is a necessary path to address the complex and changing global environmental situation and promote sustainable development (Hickel, 2020 [8]; Zhang & Zhu, 2022 [9]).

Although the proposal of the planetary boundaries framework provides us with ideas for seeking the combination of environmental sustainability and human well-being, there is still an important problem to be solved, that is, regional differences. At present, the situation of global economic development and environmental protection is severe, with the conflict between them becoming increasingly apparent (Osuntuyi & Lean, 2022 [10]). Especially in developing countries, how to balance economic development and environmental protection has been a hot topic (Leal Filho et al., 2019 [11]). This requires us to focus not only on comparative sustainable development at the national level but also to discuss the heterogeneity of sustainable development at the sub-national level. This is because a national composite indicator may not reflect the regional sustainable development profile.

China has experienced rapid economic growth for nearly four decades, and the level of human development has been continuously improved. According to the Human Development Report 2020 published by the United Nations Development Programme (UNDP), China ranks 85th, with a specific value rising from 0.758 to 0.761. A country’s sustainable development includes not only development in the economic and social fields, but also the reduction of resource consumption and improvement of environmental quality (Liao et al., 2020 [12]). However, China’s economic growth mainly follows the development model of high energy consumption and heavy pollution, which has a serious impact on the ecological environment (Chou et al., 2018 [1]). For example, China’s per capita carbon emission level is less than 50% of that of the United States, but the booming population, manufacturing industry, and import/export make China the world’s top carbon emitter with total annual emissions exceeding 10 billion tons (UNEP, 2020 [13]). At the same time, as the largest developing country, China faces a huge regional imbalance in economic and social welfare (Wang et al., 2020 [14]). Unbalanced and inadequate development has become increasingly prominent in China (Shi & Tang, 2020 [15]). Therefore, how to achieve a high level of human development in China under environment constraints and effectively promote the coordination of environmental protection and human development has become a hot issue. In recent years, scholars have tried to measure the effectiveness and gaps in sustainable development in each country or region by assessing the level of human development under planetary boundaries (Hickel, 2020 [8]; Zhang & Zhu, 2022 [9]). However, there is a lack of research on human development under planetary boundaries at the sub-national level in China as the largest developing country.

For China, the issue of how to protect the ecological environment while ensuring the improvement of human development is crucial in the context of the contradiction between environmental protection and economic development and the imbalance of regional development. How can we find a balance between economic development and resource and environmental protection? Does the destruction of the ecological environment significantly reduce human welfare? How to construct a more streamlined and effective human development index considering environmental pressure at the sub-national level? To answer these questions, we structure a human development index assessment system under planetary boundaries using China as a sample to quantitatively evaluate the level of human development under environmental resource constraints in 30 provincial regions of China in 2010, 2014, 2017, and 2020. The objectives of our study include (1) observing the level of economic and social development, ecological and environmental conditions, and human welfare under planetary pressure in China over the past decade, (2) exploring the heterogeneity of local sustainable development in light of China’s sustainable development performance, and (3) inspiring possible pathways for China as well as developing countries to tackle the relationship between economic development and environmental protection. This paper attempts to make academic contributions in the following aspects. First, it innovatively constructs a system for assessing the human development index under planetary pressure based on the planetary boundaries which expands the research and application of the planetary boundaries framework and the human development index. Both the planetary boundaries framework and the HDI have a singularity that considers only one aspect of the environment, economic or social development. Now, we have combined the two approaches to enrich the scope of planetary boundaries and HDI in application. The model is also designed from the principle that breaking the carrying capacity of the environment will be “punished”. This will help develop and explore measurement models that form a global consensus and facilitate enhanced international communication.

Additionally, this study discusses sustainable development heterogeneity at the sub-national level, which helps inform policy initiatives to optimize regional sustainable development. The HDI is widely used for country-level evaluations, and it has been gradually enriched from the regional perspective. However, the application of the planetary boundaries framework is still mainly at the global and national levels, and the research from the regional perspective is relatively few. Due to the difficulty of data collection and indicator measurement, some global consensus indicator systems are difficult to account for from the regional level. Therefore, this paper selects 30 provincial regions which can further enrich the quantitative calculation method of sustainable development level and provide a scientific basis for evaluating regional sustainable development level.

Finally, this article is useful for reflecting on the relationship between environmental protection and economic development in developing countries. Based on the concept of strong sustainability, we analyze the spatial and temporal variation of human development levels in each province under the adjustment of environmental constraints in the 11th, 12th, and 13th Five-Year Plans stages, which is helpful to observe the regional disparities, the stages, and evolutions of sustainable development in China. The sustainable development advancement of China in recent years has been remarkable, not only in improved economic levels but also in the practical value of ecological civilization. By examining the spatial and temporal changes of HDI in the framework of planetary boundaries at the provincial level in China, we can improve the understanding of the regional sustainable development gap in China’s environmental protection and human welfare, provide targeted and operational policy recommendations for China’s sustainable development, and help enrich regional sustainable development practice programs.

2. Literature Review

In 1990, the United Nations Development Programme (UNDP) famulated the Human Development Index (HDI) as a practical application to assess the ultimate criteria for a country’s development which are people and their capabilities, rather than economic growth. This index is widely used because it breaks the limit of GDP in terms of sustainability analysis, happiness studies, social choice, and equitable distribution (Resce, 2021 [16]). The HDI is still a crucial tool for evaluating a country’s development today.

We have retrieved relevant articles from both SCI and SSCI databases in the web of science and have identified an overall increasing tendency of articles with the keyword “Human Development Index” (shown in Figure 1a) (retrieved as of 7 December 2022). These studies are mainly focused on public environmental occupational health, environmental sciences, economics, social sciences interdisciplinary, and other fields (as shown in Figure 1b). SOCIAL INDICATORS RESEARCH, SUSTAINABILITY, and PLOS ONE are the top three journals in terms of number. We observed that the HDI cross-fertilized with several fields of study as the research progressed. For example, some scholars have applied the HDI to discussions on the relationship with diseases (Fidler et al., 2018 [17]; Palamim et al., 2022 [18]), the effectiveness of poverty reduction policies (Dai et al., 2021 [19]), and coordinated regional development (Permanyer & Smits, 2020 [20]; Yero et al., 2021 [21]; Resce, 2021 [16]). In particular, the integration with environmental sustainability has become a hot topic (Türe, 2013 [22]; Jain & Jain, 2013 [23]; Biggeri & Mauro, 2018 [24]).

Planetary boundaries framework was put forward by a team of scientists led by Johan Rockström from Stockholm University, which outlined a safe operating space for human beings (Rockström et al., 2009 [6]; Steffen et al., 2015 [7]). The framework contains nine processes, including climate change, changes in biosphere integrity, stratospheric ozone depletion, ocean acidification, biogeochemical flows (P and N cycles), land-system change, freshwater use, atmospheric aerosol loading, and introduction of novel entities. Some scholars attempt to use this approach to measure the environmental sustainability of global, countries, companies, and other activity subjects (Cole et al., 2014; Häyhä et al., 2016; Chandrakumar et al., 2019; Ding et al., 2020; Parsonsova & Machar, 2021 [25,26,27,28,29]) or combine this with issues regarding human rights, social justice, human health, and food security (Dearing et al., 2014; Gerten et al., 2020; Donges et al., 2021; Meijaard et al., 2022 [30,31,32,33]).

The innovative development demand of HDI and the strong environmental constraint of planetary boundaries inspired scholars to take planetary boundaries as an important human development constraint for development assessment. The 2020 Human Development Report proposes the planet pressure-adjusted Human Development Index (PHDI) should be used as a navigational mark for the Anthropocene to encourage human beings to reduce stress on the planet while developing. We searched articles using the formula “TS= ‘Human Development Index’ and ‘planetary boundaries’”. Only five articles measured planetary boundaries in combination with HDI. Among them, Zhang et al. (2021) measured PB-HDI values for 142 countries in 2015. They argue that crossing planetary boundaries leads to a decline in the level of economic and social development [34]. Hickel (2020) proposed the adjusted Sustainability Development Index (SDI), which includes two key ecological impact variables, carbon dioxide emissions, and material footprint, as a strong sustainability indicator to measure a country’s eco-efficiency in achieving human development [8]. Biggeri & Ferrone (2022) devised the Children’s Sustainable Human Development Index (CSHDI) based on planetary boundaries as a measure of environmental sustainability [35].

According to the existing research, scholars have tried to combine planetary boundaries and HDI to build a new assessment index system. However, the present indicators are still at the global or national level, and there is a lack of in-depth studies for single countries at the sub-national level, which may lead to regional development disparity being concealed in the national-level HDI assessment; and equity among countries or regions at different development stages being neglected in global-level planetary boundaries accounting. Meanwhile, the currently constructed HDI under planetary boundaries contains a limited number of ecological boundary types.

Therefore, to provide both a simplified and representative measure of the level of human development under earth stress, this paper conducts spatial-temporal observations of the true level of human well-being in Chinese provinces based on spatial heterogeneity analysis. On this basis, we further propose recommendations to promote sustainable development. Based on the literature review and the gaps of existing studies, we mainly contribute in the following aspects. First, we construct a human development index under planetary pressure that entails penalties for exceeding ecological boundaries, and explain the components and the calculation formula of this index in a detail (in Section 3 Data and Methods). Then, we separately investigate the traditional human development index and the proposed planetary boundary index to have a multi-dimensional comprehension of human economic and social development and resources and environment in each province of China over the past decade. And we further discuss the variations of conventional HDI after adding the planetary boundary constraint to deepen the interpretation of the encompassing relationship between economic development and resource environment (in Section 4 Results and Discussion). Finally, the main conclusions, research value, limitations, and future research directions of the article are summarized (in Section 5 Conclusions). The research framework of this paper is as follows (Figure 2).

3. Data and Methods

3.1. Calculation Method of HDI

The HDI measures the overall economic and social development of a country or region in life longevity, knowledge acquisition, and living standard. The three dimensions constitute the health index ($I_{life}$), the education index ($I_{edu}$), and the income index ($I_{income}$).

The health index is converted from Per Capita Life Expectancy (Equation (1)):

$$I_{life}=\frac{PCLE-PCL{E}_{Min}}{PCL{E}_{Max}-PCL{E}_{Min}}$$

(1)

To facilitate horizontal comparison, we set the minimum $PCLE$ value as 20 (Liu et al., 2020 [36]) and the maximum $PCLE$ value as 84.9 (see Human Development Report 2020 [37]).

The education index is calculated by taking the arithmetic mean of average years of education (AYE) and expected years of education (EYE). In this case, the expected years of education are obtained by multiplying the gross enrollment rate at each stage with the number of education years at that stage. For example, the duration of education at the primary level is 6 years, so the expected year of education is represented by using the multiple of the primary gross enrollment rate and 6 for that level, similarly for other education levels. The indexing formula is:

$$I_{edu}=\frac{AYE+EYE}{2}$$

(2)

EYE = PR × 6 + MR × 3 + HR × 3 + UR × 4

(3)

where PR, MR, HR, and UR represent the primary school, middle school, high school, and university gross enrollment rate, respectively.

The expected years of education are estimated according to the net enrollment ratio or gross enrolment ratio at all levels of education. Since the nine-year compulsory education was fully implemented, the national net enrollment rate of primary school-age children and the gross enrollment rate at the junior middle school level reached 99.96% and 102.5% by 2020, respectively. Therefore, we both set the elementary school net enrollment rate and the junior middle school net enrollment rate as 100% referring to the approach in a special edition of “China human development report—40 years of China’s human development in historical transformation: towards a sustainable future”. The average years of education and the expected years of education are standardized by the same method as Equation (1). According to the Human Development Report 2020, the maximum observed values of average years of education and expected years of education are 14.2 and 22, respectively.

The income index ($I_{income}$) is obtained by converting per capita GDP. We used China’s per capita GDP and per capita national income (GNI) data obtained from the World Bank database to calculate the conversion factor, and then use the product of per capita GDP and the conversion factor to estimate the per capita GNI of each province, as shown in Equation (4).

$$I_{income}=\frac{\ln \left(GNI\right)-\ln {\left(GNI\right)}_{Min}}{\ln {\left(GNI\right)}_{Threshold}-\ln {\left(GNI\right)}_{Min}}$$

(4)

We set a threshold of $20,000 for income per capita, following Hickel (2020) [8]. Zhang et al. (2021) explained that the setting of this threshold was not only the guarantee for enriching material life but also the requirement to realize the decoupling of income and ecological environment impact [34]. The value of the assessment object that exceeds the threshold is set to 1. And China’s GDP per capita and GNI (measured by PPP dollar price in 2011) are converted based on the data released by the World Bank database.

The HDI is calculated by Equation (5),

$$HDI=\left(I_{life}\times I_{edu}\times I_{income}\right){ }^{1/3}$$

(5)

3.2. Calculation Method of PB Index

We selected five boundary dimensions including climate change, freshwater use, land use, nitrogen cycle, and phosphorus cycle to construct the corresponding Overshoot Index (OI). OI represents the disparity between the actual value of eco-physical processes and the planetary boundaries. For example, Equation (6) calculates the extent to which CO₂ emissions exceed the climate change boundary.

$${\mathrm{OI}}_{c{o}_2}={\mathrm{co}}_2-{\mathrm{PB}}_{c{o}_2}$$

(6)

Next, we construct the relative planetary boundary performance index ($P_j$) for a single boundary. The $P_j$ is an inverse index that the smaller the degree of overshoot, the larger the index value. And if there is no ecological overshoot, the $P_j$ value is 1. Therefore, the $P_j$ is between 0 and 1.

Equation (7) shows the formula of $P_j$ ¹,

$$P_j=1-\frac{OI-O{I}_{Min}}{O{I}_{Max}-O{I}_{Min}}$$

(7)

where $P_j$ represents the disparity between the actual value of eco-physical processes and the planetary boundaries. For example, Equation (7) calculates the extent to which CO₂ emissions exceed the climate change boundary.

Scholars have assigned environmental boundaries either from a consumption or production perspective. Croft et al. (2018) [38] and Shaikh et al. (2021) [39] argue that production-based assessments are relevant to quantifying the environmental impacts of goods and services used by humans. Both consumption and production-based perspectives are essential for sustainability assessment. We therefore downscale the boundaries in terms of regional production. And the planetary boundaries set safety boundaries from a global scale. The application of regional or local carrying capacity accounting is still in the exploration stage which mainly includes population or land-based allocation methods. There are local thresholds of resource endowment and environmental capacity in the selected boundaries. For example, nitrogen and phosphorus are active compounds in agricultural fertilizers, so it seems more reasonable to express them by unit area than per capita. Therefore, it is more beneficial to allocate boundaries on the area of a specific region to reveal the regional resource carrying capacity, the environmental constraints, and the impact of production activities on the environment. With regard to the data availability at the sub-national level and the environmental impact on human social welfare at the macro level, the planet area boundary values for the five dimensions are set as follows (

Table 1. The setting of per capita Planetary boundaries.

Biophysical Indicator	Planetary Boundaries	Per Area Unit Boundary 2
Carbon dioxide emissions	1600 GtCO2 (From 2000 to 2099)	97.08 t/km2
Freshwater consumption	4000 km3 per year	26,845.64 m3/km2
Nitrogen fertilizer consumption	62 teragrams per year	4.44 t/km2
Phosphorus fertilizer consumption	6.2 teragrams per year	0.44 t/km2
Arable land area	15% of global available land	0.15

).

The UNEP (2012) estimates that the yearly budget is 16 GtCO2/y, in line with the 2 °C objective, which is compatible with the global boundaries for climate change. These estimates are also commensurate with the values recently reported by Rogelj et al. (2019) [40], who estimated and tracked the remaining carbon budget for stringent climate targets. The unit area nitrogen and phosphorus boundary are calculated using the nitrogen and phosphorus fertilizer consumption per square kilometer of arable land area. We calculate the land change boundary using the national per capita arable land area, drawing on Zhang et al. (2021) [34].

Many scholars utilized the coefficient method or input-output method to calculate CO₂ emission (Morán & del Río González, 2007; Tsai et al., 2018; Böhringer et al., 2021; Taheripour et al., 2022 [41,42,43,44]). This paper measures the total CO₂ emissions of various places based on the total energy consumption by region and species in China Energy Statistical Yearbook, using the IPCC National Greenhouse Gas Emissions Inventory (2006) as a reference [45]. The CO₂ emission is calculated by Equation (8),

$${\mathrm{co}}_2={\displaystyle \sum }_{i=1}^n{T}_i\times {\alpha }_i$$

(8)

where $T_i$ represents the consumption of each carbon source, and ${\alpha }_i$ represents the carbon emission coefficient of the corresponding carbon source. We measured 8 major energy categories, including coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil, and natural gas. The CO₂ emission conversion coefficients of the eight energy categories are shown in

Table 2. Conversion coefficients of CO₂ emissions for various energy sources (IPCC National Greenhouse Gas Emissions Inventory, 2006 [45]).

	Coal	Coke	Crude Oil	Gasoline	Kerosene	Diesel	Fuel Oil	Natural Gas
${\alpha }_i ({\mathrm{co}}_2/$ kg)	1.9003	2.8604	3.0202	2.9251	3.0179	3.0959	3.1705	2.1622

.

The freshwater per capita consumption was calculated using the total water consumption of the province published in the China Water Resources Bulletin. Per capita consumption of nitrogen fertilizer, phosphorus fertilizer, and per capita arable land area was calculated based on the total nitrogen and phosphorus fertilizer consumption and total regional arable land area in the National Statistical Yearbook for 2010, 2014, 2017, and 2020.

The relative planetary boundary performance index ($P_j$) for the biophysical processes of freshwater use, land use, nitrogen cycle, and phosphorus cycle were calculated according to Equations (6) and (7). Finally, referring to the practice of Zhang et al. (2021) [34], Human Development Report 2020 [37], and Human Development Report 2022 [46], we continue to simplify the operation for the integrated index calculation. The planetary boundaries index ($E{I}_{PB}$) was calculated as the arithmetic mean of the 5 overshoot indexes presented before, given by Equation (9),

$$E{I}_{PB}=\frac{P_{c{o}_2}+P_{water}+P_{land}+P_N+P_P}{5}$$

(9)

3.3. Calculation Method of PB-HDI

The HDI implies the basic needs of human development, while planetary boundaries are the basic safe space for human survival and development. To achieve human prosperity in a safe operating space, a simultaneous balance of ecology, economy, and society is required. The PB-HDI is obtained by multiplying the HDI with the planetary boundaries index ($E{I}_{PB}$). The Planetary boundaries framework is a restrictive factor in human development. Exceeding planetary boundaries means that the ecological environment may face significant damage, which likewise leads to a decrease in the level of human development welfare and the PB-HDI value.

Following the approach of Zhang & Zhu (2022) [9], the HDI under planetary boundaries is calculated by Equation (10),

$$\mathrm{P}\mathrm{B}-\mathrm{H}\mathrm{D}\mathrm{I}=HDI\times E{I}_{PB}$$

(10)

To investigate the dynamic performance of regional human development and ecological construction, the values of PB-HDI were divided into different groups. According to the classification criteria of the United Nations Development Programme in 2016, the HDI below 0.550 represents low human development, 0.55 to 0.699 represents medium human development, 0.7 to 0.799 represents high human development, and above 0.800 represents very high human development. Therefore, we refer to this approach to group the planetary boundaries index ($E{I}_{PB}$) and PB-HDI value, which can improve the comparability of the PB-HDI ranking.

3.4. Calculation Method of CV

The coefficient of variation (CV), defined as the ratio of the standard deviation to the mean, is also calculated. It reflects the differences between groups, higher values indicate greater imbalance, given by Equation (11),

$$CV=\frac{\sqrt{\frac{1}{N}{{\displaystyle \sum }}_{i=1}^N{\left(x_i-\mu \right)}^2}}{\mu }$$

(11)

3.5. Data Collection and Processing

The HDI under the planetary boundaries is calculated using data from 30 provinces in China (the data of Tibet, Taiwan, Hong Kong, and Macau are lacking) for 2010, 2014, 2017, and 2020, which are manually compiled from the 2010–2020 statistical yearbooks, statistical bulletins, and Five-Year Plans of each province. The data on life expectancy per capita are obtained from the Eleventh to Fourteenth Five-Year Plans. The data on the average years of education are obtained from the average years indicator of education of the population aged 15 and above in the Sixth and Seventh National Census of China. The data on gross enrollment rates for high school and higher education are from the Eleventh to Fourteenth Five-Year Development Plans and the Eleventh to Fourteenth Five-Year Education Plans. Data on total population, GDP, GDP per capita, arable land area, and total nitrogen and phosphorus fertilizer consumption by province are derived from 2010, 2014, 2017, and 2020 statistical yearbooks and statistical bulletins. Energy consumption data are sourced from the China Energy Statistical Yearbook, and freshwater consumption per capita is accessed from the China Water Resources Bulletin in 2010, 2014, 2017, and 2020. To further explore the spatial disparity in the development process, 30 provincial regions are divided into the east, central, west, and northeast regions ³. At the same time, we used the ArcGis 10.4 software to draw distribution maps to observe the dynamics of the PB-HDI in the 30 provincial regions more visually and intuitively.

4. Results and Discussion

4.1. Spatio-Temporal Observation of Index

4.1.1. Spatio-Temporal Observation of HDI

China has undergone tremendous changes since its reform and opening up with China’s Human Development Index rising from 0.410 in 1978 to 0.761 in 2019. It is the only country that has risen from a low human development group to a high human development group since the first Human Development Report in 1990. Based on the results, we drew a dashboard which allows a visual comparison of the status of different provinces according to different colors.

As

Table 3. PB, HDI, and PB-HDI indexes dashboards of various provinces over the years.

Year	2010			2014			2017			2020
Province	HDI	PB	PB-HDI	HDI	PB	PB-HDI	HDI	PB	PB-HDI	HDI	PB	PB-HDI
Beijing	0.879	0.481	0.423	0.886	0.553	0.490	0.922	0.605	0.558	0.931	0.592	0.551
Tianjin	0.838	0.348	0.291	0.856	0.374	0.320	0.881	0.455	0.401	0.886	0.455	0.403
Hebei	0.709	0.487	0.345	0.745	0.499	0.372	0.760	0.516	0.392	0.804	0.593	0.477
Shanxi	0.714	0.624	0.446	0.743	0.635	0.471	0.767	0.653	0.501	0.795	0.707	0.562
Inner Mongolia	0.759	0.716	0.544	0.795	0.696	0.553	0.805	0.688	0.554	0.818	0.843	0.689
Liaoning	0.775	0.662	0.513	0.825	0.662	0.546	0.806	0.683	0.551	0.820	0.699	0.573
Jilin	0.742	0.753	0.558	0.786	0.745	0.586	0.797	0.756	0.602	0.799	0.799	0.638
Heilongjiang	0.724	0.764	0.553	0.765	0.747	0.571	0.767	0.749	0.575	0.784	0.803	0.630
Shanghai	0.855	0.328	0.280	0.869	0.393	0.342	0.895	0.436	0.390	0.897	0.491	0.441
Jiangsu	0.781	0.270	0.211	0.823	0.288	0.237	0.833	0.319	0.266	0.857	0.342	0.293
Zhejiang	0.770	0.562	0.433	0.828	0.587	0.486	0.824	0.605	0.498	0.850	0.616	0.524
Anhui	0.659	0.518	0.341	0.724	0.519	0.376	0.742	0.531	0.394	0.800	0.587	0.470
Fujian	0.742	0.456	0.338	0.785	0.448	0.352	0.801	0.470	0.376	0.842	0.418	0.352
Jiangxi	0.666	0.647	0.431	0.731	0.636	0.465	0.749	0.650	0.487	0.795	0.697	0.554
Shandong	0.747	0.458	0.342	0.795	0.469	0.373	0.808	0.484	0.391	0.825	0.495	0.408
Henan	0.684	0.346	0.236	0.733	0.352	0.258	0.752	0.380	0.286	0.788	0.449	0.354
Hubei	0.714	0.446	0.318	0.769	0.461	0.354	0.796	0.498	0.397	0.831	0.555	0.461
Hunan	0.696	0.581	0.404	0.746	0.587	0.438	0.779	0.601	0.468	0.807	0.637	0.514
Guangdong	0.767	0.469	0.360	0.797	0.466	0.371	0.829	0.449	0.372	0.848	0.392	0.332
Guangxi	0.662	0.659	0.436	0.714	0.639	0.456	0.742	0.639	0.474	0.766	0.622	0.476
Hainan	0.696	0.670	0.466	0.747	0.637	0.476	0.772	0.641	0.495	0.806	0.617	0.497
Chongqing	0.702	0.576	0.404	0.764	0.594	0.454	0.798	0.600	0.479	0.830	0.606	0.503
Sichuan	0.664	0.636	0.422	0.725	0.643	0.466	0.740	0.646	0.478	0.789	0.675	0.532
Guizhou	0.576	0.723	0.417	0.667	0.741	0.494	0.693	0.739	0.512	0.745	0.798	0.595
Yunnan	0.596	0.776	0.463	0.663	0.749	0.497	0.683	0.740	0.506	0.763	0.769	0.587
Shaanxi	0.714	0.689	0.492	0.766	0.672	0.515	0.788	0.676	0.533	0.822	0.663	0.545
Gansu	0.622	0.698	0.434	0.682	0.684	0.466	0.689	0.699	0.481	0.725	0.725	0.526
Qinghai	0.638	0.802	0.512	0.702	0.780	0.547	0.701	0.789	0.553	0.753	0.815	0.614
Ningxia	0.684	0.541	0.370	0.738	0.533	0.394	0.766	0.536	0.410	0.798	0.566	0.452
Xinjiang	0.684	0.638	0.436	0.728	0.499	0.363	0.755	0.484	0.366	0.776	0.652	0.506

shows, with the continuous implementation of the five-year plan, the HDI values of all provinces have increased. And the number of provinces entering the very high human development group and the high human development group is increasing. The provinces with low human development index had disappeared completely by 2020. Compared to 2010, the number of provincial regions in 2020 entering the very high HDI has increased significantly. Provincial regions with the highest HDI scores including Beijing, Shanghai, Tianjin, Jiangsu, and Zhejiang, are mainly in the east. These provincial regions have stronger overall economic strength, and their education level and medical security level are at the forefront of China. Beijing, Shanghai, and Tianjin have reached high HDI at an early stage, with Beijing’s HDI reaching 0.931 in 2020. Provinces with low HDI scores, including Gansu, Xinjiang, Guizhou, and Qinghai, are clustered mainly in the western region. The western region has a relatively low HDI, and its human well-being has improved in line with the overall development of China. Chongqing and Shaanxi have been at the very high HDI stage and are representative. Currently, most of the provincial regions in the central and northeastern regions are at a high level of development stage.

As revealed in

Table 4. Regional variation coefficient (%).

Year		2010			2014			2017			2020
Region		HDI	PB	PB-HDI	HDI	PB	PB-HDI	HDI	PB	PB-HDI	HDI	PB	PB-HDI
RCV	total	9.80	24.98	21.87	7.33	22.71	20.59	7.27	20.56	18.37	5.51	21.36	18.93
	east-central	8.94	24.51	21.37	6.71	21.77	20.35	6.75	19.35	18.98	4.98	20.14	18.87
	east-northeast	7.23	30.65	27.49	5.52	26.92	25.36	6.06	24.07	22.43	5.08	26.18	23.62
	East-west	11.27	26.24	20.15	8.37	23.47	19.22	8.46	21.09	17.23	6.43	22.99	19.76
	central-northeast	5.20	23.22	26.03	4.26	21.97	24.07	2.99	20.14	21.29	1.88	17.49	17.04
	central-west	6.83	18.62	17.47	4.92	18.29	17.01	5.21	17.14	15.38	3.84	15.40	14.36
	northeast-west	8.75	10.93	12.85	6.65	12.50	13.27	6.02	12.88	12.66	4.07	12.38	12.07
ICV	east	7.86	25.64	22.11	5.94	22.11	21.17	6.31	19.48	19.91	4.72	19.93	19.99
	Central	3.41	21.85	21.93	2.15	20.95	20.75	2.69	19.02	19.12	1.90	16.01	15.82
	northeast	3.46	7.70	4.59	3.84	6.75	3.52	2.58	5.51	4.50	2.26	7.69	5.77
	west	8.05	11.60	11.24	5.86	13.43	12.30	6.08	13.80	11.79	4.36	13.17	12.51

RCV and ICV represent the coefficient of variation between regions and the intra-regional coefficient of variation, respectively.

, the coefficient of variation of HDI among regions decreased from 9.8% in 2010 to 5.51% in 2020, reflecting that the overall disparity keeps decreasing. However, there are still gaps between regions, especially between the eastern and the western regions. The coefficient of variation between the eastern and western regions in 2020 is 6.42% while the coefficient of variation is 11.27% in 2010. It is still the greatest disparity between eastern and western regions although the coefficient of variation has decreased by about 43%. The intra-regional coefficient of variation of the eastern and western regions both exceeds 4% in 2020. Among the eastern regions, Hebei has the lowest HDI and its educational resources are relatively low which needs further improvement. The intra-regional coefficient of variation of the central region is the smallest, implying a more balanced level of human development in the central provinces. The coefficients of variation of the health index, education index, and income index among regions in 2020 are 3.71%, 6.55%, and 8.68%, respectively (

Table 5. The coefficient of variation of each boundary in 2020.

	${\mathit{I}}_{\mathit{l}\mathit{i}\mathit{f}\mathit{e}}$	${\mathit{I}}_{\mathit{e}\mathit{d}\mathit{u}}$	${\mathit{I}}_{\mathit{i}\mathit{n}\mathit{c}\mathit{o}\mathit{m}\mathit{e}}$	${\mathit{P}}_{\mathit{c}{\mathit{o}}_2}$	${\mathit{P}}_{\mathit{w}\mathit{a}\mathit{t}\mathit{e}\mathit{r}}$	${\mathit{P}}_{\mathit{N}}$	${\mathit{P}}_{\mathit{P}}$	${\mathit{P}}_{\mathit{l}\mathit{a}\mathit{n}\mathit{d}}$
CV (%)	3.71	6.55	8.68	55.3	35.81	32.17	35.37	44.41

). The western region still needs to continuously improve health, education, and income levels.

From the perspective of the health index, the national per capita life expectancy has reached 77.93 years in 2020. However, in 2020, Gansu, Qinghai, Xinjiang, and Guizhou are 74, 73.7, 74.35, and 74.5 respectively, which don’t reach the national average level. From the perspective of education indices, China’s education achievements are remarkable. The gross enrolment ratio from primary school to high school has increased significantly, but the regional gap in the gross enrolment ratio in higher education is obvious. The national gross enrollment rate for higher education in 2020 is 54.4%, while it is less than 50% in many places such as Xinjiang, Tibet, Guangxi, and Gansu. Thus, with the implementation of the Western Development Strategy and the successful completion of the poverty eradication target, the level of public services such as education and culture, medical and health care, and social security in the western region has continued to improve, especially in Sichuan and Chongqing. But the HDI of most western regions is still low.

4.1.2. Spatio-Temporal Observation of PB

In Table 3, the number of dark orange increases while the number of white color blocks decreases, implying that the number of provinces with high ecological development levels progressively increases, while the number of provinces with low ecological development level steadily declines from 2010 to 2020. From 2010 to 2020, the number of provinces entering the very high level rises from 1 to 3 and the low development level falls from 12 to 7.

The coefficient of variation of the planetary boundary index is larger in the eastern provinces, still reaching 19.93% in 2020 (Table 4). This indicates that the eastern provinces have significant differences in their performance in ecological development. Eastern provinces such as Tianjin, Jiangsu, and Guangdong are still at the low development level stage, which is a reflection of the huge ecological pressure faced by the rapid development process of eastern Chinese cities. Meanwhile, other eastern provinces, such as Zhejiang, Hainan, and Beijing, have entered the medium development level. Zhejiang shows a steady upward trend; at the same time, Beijing and Hainan show a small decline by 2020. This is partly because these regions have a better resource foundation, and because of its better balance between development and the environment. From 2010 to 2020, the number of provinces in the eastern region that have entered the medium development level from the low level has increased, which is an important reflection from rapid development to high-quality development due to the adjusting of economic development model in the process of building ecological civilization.

Liaoning, Jilin, and Heilongjiang continue to maintain a high level of development, with Heilongjiang entering a very high development stage in 2020. This may be due to the significant resource advantages that have the important natural barriers to the northeastern ecosystem including Changbai Mountains and Xing’an Mountains. And with the adjustment of industrial structure, their ecological environment is maintained. The average score of the western regions is higher than that of the eastern regions. Most western provinces such as Inner Mongolia, Qinghai, Gansu, and Yunnan also maintain a greater ecological environment. This is attributed to the rich natural resources in the western regions, and the stronger resource endowment enhances the planetary boundary accounting advantage of these regions. Qinghai scores the highest as the most fertile region in Eurasia with over 60 billion cubic meters of pure and high-quality water flowing out of Qinghai. And it also has the highest concentration of biodiversity among the high-altitude regions in the world. Therefore, Qinghai shows its unique advantages.

The dispersion of the PB indicator shows an overall decreasing trend, with the coefficient of variation rebounding in 2020, rising to for 21.36%, as shown in Table 4. Although the decline from 2010 to 2020 is more than 15%, it is still about twice the coefficient of variation of HDI which indicates that the impact on environmental ecology shows greater regional differences and unevenness. The northeast region has the smallest coefficient of variation, due to the more similar resource base, less difference in industrial structure and obvious industrial agglomeration. In 2020, the CV of climate change boundary is higher than other PB boundary indicators, probably owing to the differences in regional technology level, energy use structure, and industrial structure layout, which make the regional carbon emission level more different.

4.1.3. Spatio-Temporal Observation of PB-HDI

As shown in Table 3, the white indicates that the PB-HDI index is at a low level of development. The number of provinces in the low-level development group (the white block in the table) of the PB-HDI index has decreased significantly; at the same time the number of provincial areas in the medium level of development increases, from 2 in 2010 to 10 in 2020. Figure 3 depicts the spatial distribution of PB-HDI scores in 2010, 2014, 2017, and 2020. Most provinces are at a low level in 2010. This can be explained by the fact that although some provinces still have a high level of ecological environment at this time, their economic and social development is relatively backward, resulting in a low overall score. After a decade of development, 1/3 of the provinces have entered the medium development stage. The western provinces show a trend of continuous economic and social rise, while the eastern provinces show a trend of economic restructuring and continuous catching up in ecological protection.

The value of HDI and the ranking tends to stabilize year by year, and then the addition of the planetary boundaries index has a significant impact on the overall ranking. Central and western regions like Chongqing, Guizhou, Sichuan, Shanxi, and Qinghai have moved up in HDI rankings as a result of recent significant advancements in ecological construction and resource endowment advantages despite their lower HDI rankings. By comparing the PB-HDI composite index values of some western provincial regions, we find that their human development levels are under planetary pressures, resulting in the lower PB-HDI, such as Ningxia and Xinjiang. In contrast, in Tianjin, Fujian, and Anhui, the human development index is significantly higher than the PB level, but their development process fails to balance well the relationship between economy and environment, resulting in a decrease in the composite index. And some regions where the human development index is at a high level stage, such as Jiangsu, Fujian and Guangdong, have paid the price of environmental and ecological damage for their rapid economic development.

The coefficient of variation of PB-HDI among regions decreases slightly, from 21.87% in 2010 to 18.93% in 2020, indicating that there is still a significant regional imbalance in human development in China under planetary pressure. However, the coefficient of variation between regions increases approximately by a factor of three as assessing human development under planetary pressure, which means that considering the regional impact on the environment can better reveal the level of human wellbeing enhancement. The intra-regional as well as inter-regional variation coefficients show a decreasing trend. The largest intra-regional coefficient of variation is in the east, probably in response to the prominent problem of incompatible environmental relations exposed in the process of high economic development.

It shows that with the continuous development of the economy and society, the relationship among the economy, society, resources, and environment is modified. The gap in HDI value is steadily narrowing. And the maintenance of the ecosystem becomes the key to sustainable development. It is an inevitable trend to pursue human economic and social development within planetary boundaries. The higher level of economic and social development at the cost of exceeding planetary boundaries reduces the degree of sustainable development. A more reasonable sustainable path is to pursue high levels of economic and social development within planetary boundaries. China is a developing country, so it still needs to improve the living standards of its citizens to fully satisfy the demands of “sustainability” and “development” at this stage. In other words, lower levels of economic and social development within the planetary boundaries are also discouraged. Therefore, the provinces with the low HDI index such as Qinghai, Guangxi, and Guizhou, still need to focus on the development process to achieve the goal of common prosperity in the future.

4.2. Discussion

That the growth rate of the HDI has declined across provinces at the current stage, and the regional disparities are converging. This corresponds to the results of Li et al. (2022) [47] and Wang et al. (2022) [48]. Eastern provinces such as Beijing (0.931), Shanghai (0.897), Tianjin (0.886), Jiangsu (0.857), and Zhejiang (0.850) are in the top echelon of HDI rankings, while western provinces such as Gansu (0.725), Guizhou (0.745), and Qinghai (0.753) are at the bottom. The regional HDI gap is small, but the large gap in education and income level is the major cause for the lagging ranking of some western provinces (Li et al., 2022 [47]). The coefficient of variation between the income index and the education index is 2–3 times higher than that of the health index. This is consistent with the results of our HDI single indicator observation. The income index and education index of Guizhou ($I_{edu}$ = 0.612, 30/30), Qinghai ($I_{edu}$= 0.620, 29/30), and Gansu ($I_{edu}$= 0.627, 27/30) rank at the bottom in 2020 (see Appendix A). While maintaining the high-quality development of eastern provinces such as Beijing, Shanghai, and Zhejiang, we should make up for the shortcomings in the construction of education and social basic security in the western regions (Xu et al., 2020 [49]).

Resource endowments, industrial structure, technological level, and ecological protection measures among regions are significant reasons for the large disparities in the planetary boundaries index. These factors can noticeably affect the regional ecological civilization level, and the ecological pressure varies from region to region (Gai et al., 2020 [50]; Zhang et al., 2022 [51]). The ecological pressure is different in different regions. For example, some eastern provinces with rapid economic development such as Guangdong, Fujian, and Jiangsu have reduced industrial environmental pollution by transferring industries, making them dominated by service industries However, these areas are densely populated and are places where consumption is concentrated. When the economic development model is not adjusted in a timely manner, the pursuit of economic development will exceed the regional resource carrying capacity. Qinghai and Guizhou have a comparatively advantageous ecological resource base and a moderate ecological deficit, with PB index ranking second and fourth respectively while 28th and 29th in HDI. So their economic development should be promoted under the precondition of environmental protection (Li et al., 2022 [52]).

Therefore, we should explore differentiated development paths based on the obvious differences in resource endowment, ecological background, and development level. And it is necessary to reduce the degree of imbalance within the eastern region and accelerate the pace of networking and industrialization of ecological resources in the eastern region. At this stage, the trend of industrial transfer is obvious because of the huge resource potential, which will lead to large differences in industrial layout, resource consumption, and environmental impact between regions (Ren et al., 2019 [53]). Thus, we need to pay attention to realizing the value of ecological products in this process and promote the transformation of ecological advantages to economic advantages in some provinces and cities in western regions. For example, the western region promotes carbon trading in the process of the industrial undertaking (Chen et al., 2020 [54]). Western regions receive financial compensation and technical support from economically developed regions, while actively promoting the orderly implementation of pollution and carbon emission reduction.

Resources and the environment have a strong constraining effect on human economic and social development. Sustainable development is the result of the interaction between the economy and the environment, and independent development of either side is not conducive to the long-term improvement of human welfare. High levels of development beyond the boundaries and lower levels of development within the boundaries are discouraged (Zhang & Zhu, 2022 [9]). Raworth (2012) combines nine planetary boundaries system processes with eleven social base indicators, which creates a “safe and just (operating) space” [55]. And this study and the “doughnut” theory share the idea that the “doughnut” mode, which forms the outer circle of economic and social development with resources and environment, is the best development path, which provides thoughts for exploring the balanced development relationship among humans, nature, and environment (Saunder & Luukkanen, 2022 [56]). We need to establish a dynamic tracking system for the PB-HDI level to better understand the stage and gaps of the PB-HDI level in each province in the future. Strengthen the communication and cooperation of construction methods among provinces in health, education, economy, and ecological environment. It is also necessary to promote the rational allocation of resources among cities of all levels and types. In terms of ecological environment construction, there is a need to break the shackles of administrative divisions, strengthen joint governance between regions, and gradually drive neighboring provinces to improve PB-HDI levels, ultimately achieving regional balanced development.

According to the planetary boundaries index, most provinces have exceeded the climate change boundary. Only Inner Mongolia, Qinghai, and Xinjiang do not exceed the CO₂ boundary in 2020. China has proposed to promote the goal of peak CO₂ emissions and carbon neutrality in multiple ways. The realization of carbon peaking and carbon neutrality is an essential way to promote high-quality economic development, resolve ecological and environmental risks, and enhance the initiative of global governance (Zhao et al., 2022 [57]). Yang et al., 2022 [58] assumed that the provinces should develop countermeasures with respect to the differences in double carbon performance. Therefore, after analyzing the changes in the planetary boundary index by province, we conclude that Shanxi, Hebei, and Inner Mongolia need to accelerate the transformation of traditional energy structures; northeastern provinces like Liaoning, Jilin, and Heilongjiang need to actively promote the development of energy alternatives and accelerate the construction of green low-carbon industrial systems; Guangdong and Hainan need to vigorously promote the development of clean energy, and the ecological and green transformation of traditional industries; Sichuan, Chongqing, and other western regions work around new energy generation and industrial structure optimization and adjustment.

In this study, we do not fully account for regional consumption of products and services. Although we “penalize” high-income regions for excessive income, the environmental impact is shifted when products and services are imported from another region through consumption. This means that the environmental impact of consumption can occur far from the place of production. In our calculations, we assess the environmental effects of regional activities in a production-based manner. It is also essential to measure the environmental impact based on production. There is environmental validity to production-based testing. The production perspective is consistent with the polluter-pays principle and can effectively measure the environmental pollution of the production process (Liu, 2015 [59]), as well as provide a degree of incentive for environmental behaviors at the production location (Duus-Otterström, 2022 [60]). There is no theory of causality that only producers cause environmental pollution, since both producers and consumers are necessary for emissions. A production-based accounting approach may lead to the impact of environmental pressures implied by consumer cities in large net imports, such as food, being one of the main drivers of many borders, to be ignored. In other words, when continuing to consider the consumption of products and services such as import and export trade, the degree of environmental impact of large consumer cities such as Beijing, Shanghai, and Zhejiang will increase and the current measured index will decline. To mitigate this situation, we chose to calculate the boundary values as well as the regional environmental and ecological performance on an area basis. In our study, when we use a per capita approach for the calculation, we find that the pressure on the environment will be underestimated due to the higher population density in these areas. The area-based approach to consider the regional resource carrying capacity can alleviate the underestimation of ecological pressure in consumer cities to a certain extent. At the same time, choosing the perspective of area and carrying out measurements from actual production activities allows for a direct assessment of the environmental impact of production activities, which is again an integral part of the environmental effects assessment. This means that both the place where the product is consumed and the place where it is produced need to be kept within the boundaries. This can further reveal that the imbalance between economic development and ecological protection in the process of rapid development will lead to a decrease in the level of human welfare.

5. Conclusions

Human has entered the Anthropocene stage. Human activities have an important impact on the stability of the Earth’s ecosystem. The relationship between social and ecological systems is increasingly intertwined at this stage. Discussing the natural and human worlds separately and ignoring the relationships between them is not reflective of the concept of sustainable development and cannot help us to deal with the current array of complex and interrelated dilemmas. Therefore, it is necessary to establish systemic thinking, considering economic development, social welfare enhancement, and ecological protection together. Human development is dynamic, so the measurement methods for human development should also be constantly updated and dynamically changed. That is why our research discusses the state of human development under planetary pressure.

This study constructs a new PB-HDI assessment system by combining HDI with five biophysical boundaries, including climate change, freshwater use, land-system change, nitrogen cycle, and phosphorus cycle. Based on the analysis of the spatial-temporal distribution of human development under planetary boundaries of 30 Chinese provincial regions in 2010, 2014, 2017, and 2020, we further used dashboards and maps to visualize the changes in space and time. The coefficient of variation is also used to analyze the regional disparities. The study concludes that: firstly, the human development index of each province gradually forms a pattern of high starting point and low growth, while the gap between regions is converging; secondly, the differences of regional planetary boundary indices are determined by various factors, such as resource endowment, industrial structure, and ecological protection initiatives; thirdly, resources and environment constitute the outer circle of economic and social development, forming a “doughnut” type of inclusion pattern, which implies high level development beyond the boundary and lower level development inside the boundary are not to be encouraged.

To measure the level of human development under planetary pressure, this study introduces the planetary boundary framework to simplify the complex process of quantifying environmental systems. This could enrich the application practice of planetary boundaries, compensate for the deficiencies of the existing HDI and planetary boundary framework in discussing regional differences, and provide inspiration for exploring the model configuration of human development level measurement considering environmental factors. Furthermore, this paper observes China’s economic and social development and ecological civilization development from multiple dimensions, which is significant for mastering the current situation of China’s sustainable development, deeply comprehending the relationship between environmental protection and economic development, and optimizing the path of regional sustainable development.

There are limitations to this study. At the data level, since the majority of data are collected from domestic yearbooks, it is inconvenient to make international comparisons for some indicators. In addition, some indicators are not continuous in statistical years, results that changes cannot be observed through consecutive time series, and the observation period of the current study is until 2020. At the model construction level, as specific data are not available, five planetary boundaries were selected from seven quantifiable planetary boundaries for assessment in this paper. Moreover, the correlation between environmental systems is not considered in this paper when totaling planetary boundary subsystems. Therefore, a simple arithmetic average was used in the calculation of the composite index. In the future, the model construction can be considered to be adjusted in terms of the interrelationships between subsystems and the weights of each subsystem. Meanwhile, given the availability of data and the preliminary attempt of model construction, we chose to analyze the regional ecological impact from the perspective of regional ecological carrying capacity. However, the environmental footprint of products and services generated by trade was not calculated based on the place of consumption in this article when constructing the ecological index measurement model. There is a definite trend to develop environmental impact measurement based on a consumption perspective. Some scholars argue that consumption-based accounting will provide a fairer and more effective environmentally distribution of the climate burden. Production-based accounting tends to underestimate the ecological impact of metropolitan areas, especially those that rely on large consumption imports of goods and services. Therefore, measuring ecological impacts from the perspective of consumption is an important direction for the future. To further compensate for the production-based deficiencies, the construct multi-regional input output tables (MRIO) method can be considered to portray the impact of human activities on consumption more comprehensively from the perspective of consumption.

As the pursuit of human well-being becomes more comprehensive, the introduction of more comprehensive and upgraded HDI can help us better understand human development and heterogeneity across countries and regions. Therefore, the HDI assessment framework will need to be continually optimized in the future, for example, by expanding it with the “doughnut” theory and SDGs. As environmental systems are complex and interrelated, future consideration could be given to exploring the inclusion of all planetary boundaries in the assessment framework to fully consider the system’s inter-connectedness and dynamics. In particular, scholars believe that trade flows may affect the environmental impact of consumption sites, so it is also an attractive direction to delve into the integration of planetary boundaries and human well-being from a consumption perspective. In addition, scholars argue that development also varies widely between cities, and that cities are more representative of regional human economic and social development under planetary pressures. Therefore, the use of more disaggregated data in the future could help explore sustainable development gaps that are masked by average provincial development levels. The assessment model construction approach in this paper provides critical perspectives and approaches for further refining assessment indicators and conducting human development level measurement under planetary pressure for a wider range of subjects (e.g., among developing countries, among major Asian regions, among cities, etc.).