Reduction Of Carbon Emission Intensity Research Articles

How an emission trading system affects carbon emissions? Evidence from the urban agglomeration in the Middle Reaches of the Yangtze River, China

The emission trading system (ETS) is one of the important policy instruments that help to achieve the “double carbon” goals, however, how the ETS affects the carbon emissions for an urban agglomeration remains uncertain. This study aims to explore the ETS’s impacts to carbon emission intensity (CEI) in Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) of China during 2006–2018. By using the Difference-in-Differences (DID) model, the positive effects of ETS to CEI reduction were confirmed, and the robustness of findings were proved via the parallel trend test, placebo test, and propensity score matching DID (PSM-DID) method. This study also recognized two mediation impacting paths, which were economic intensity improvement and green technology innovation, and provided the evidence that the ETS could further reduce the CEI through improving the economic intensity as well as the green technology innovation. Furthermore, the study investigated the heterogeneous impacts from ETS by dividing the cities within the agglomeration into different orientations. The ETS performed better in the edge cities, and the same phenomenon occurred when considering the mediation effect from economic intensity. Policy recommendations about ETS were also provided.

Does the green finance reform and innovation experimental zone policy reduce carbon emission intensity? Evidence from Chinese cities

The reduction of carbon emission intensity cannot be achieved without policy support, of which green financial policy is a major component. The main aim of this study is to discuss the causality between the green finance reform and innovation (GFRI) experimental zone policy and the intensity of carbon emissions in China. Using data from 265 Chinese cities from 2005 to 2021, this study applies a time-varying difference-in-differences (DID) model to assess the effect of the GFRI experimental zone policy on the intensity of carbon emissions. The heterogeneity of impacts and the parallel-serial mediation effect of this experimental zone policy are discussed further. The following main findings are obtained. (1) The GFRI experimental zone policy dramatically reduces carbon emission intensity, and this effect is more prominent in cities with large sizes of credit and bond finance. (2) After considering the heterogeneity of government environmental concerns and fiscal pressures, this study reveals that the effect of the GFRI experimental zone policy on reducing carbon emission intensity is weaker in cities with high numbers of environmental protection penalties and fiscal pressures. As the number of environmental penalties and fiscal pressures increases, the effect of the GFRI experimental zone policy on reducing carbon emission intensity decreases; that is, the phenomenon of environmental protection paradoxes and policy conflicts has emerged. (3) Increasing financing for green projects and decreasing the energy intensity of the industry are the primary mediating channels for GFRI experimental zone policy to reduce carbon emission intensity, but the mediating channels for boosting green innovation and promoting energy structure adjustment are not prominent. Moreover, the total effect of the GFRI experimental zone policy on reducing carbon emission intensity is 72% direct and more than 20% indirect. This study concludes that in regions with high fiscal pressure, governments should rely more on green finance to decrease carbon emission intensity. Meanwhile, a green finance policy must be more supportive of zero-carbon energy production and reduce direct environmental penalties to decrease carbon emission intensity in the future.

What is the Impact of Fiscal Technology Expenditure on Carbon Emission Reduction? Evidence from Guangdong Province in China

China has put forward the strategic goal of striving to achieve peak carbon by 2030 and carbon neutrality by 2060, and reducing carbon emission intensity is a top priority. This paper analyses the impact of local government fiscal science and technology expenditures on carbon emission intensity based on a fixed panel regression model using panel data of 21 cities in Guangdong Province from 2000 to 2019, and draws the following main conclusions: (1) Higher fiscal science and technology expenditures significantly promote the reduction of carbon emission intensity. Specifically, for every 1% increase in expenditures, the carbon emission intensity decreases by 0.221%; (2) The mechanism includes promoting the level of green innovation and promoting the level of new and high technology. Therefore, local governments should pay attention to the role of environment-friendly science and technology innovation on low-carbon development, help glow-carbon research and application transformation, strengthen the regulation of heavy polluting enterprises, and guide the capital to green industries, so as to embark a new road of high-quality economic development and carbon reduction.

Spatial–Temporal Evolution and Driving Factors of Coupling Coordination between High-Quality Urban Development and Carbon Emissions Intensity in Guangdong Province

Promoting urban green growth necessitates a dual focus on reducing carbon emissions and fostering high-quality development. However, the body of research on the coupling relationship between high-quality urban development and carbon emissions remains remarkably limited. Taking Guangdong province as an example, this study strived to establish a comprehensive evaluation system for high-quality urban development encompassing economic, societal, and ecological dimensions and further conducted an in-depth examination of the spatiotemporal pattern and driving forces of coupling coordination degree between high-quality urban development and carbon emission intensity during 2000–2017. The coupling coordination degree in Guangdong province has shown continuous growth, transitioning from moderate incoordination to moderate coordination. The coupling coordination degree showed the overall spatial distribution characteristics of “high in southeast and low in others”, with Zhuhai, Zhongshan, Foshan, Guangzhou, Dongguan, and Shenzhen as the core. Notably, technological advancement, environmental governance, and economic development emerge as pivotal factors that positively affect carbon emission intensity reduction, environmental quality improvement, and coupling coordination enhancement. This research provides valuable insights for achieving harmonized high-quality development in Guangdong province involving policies of regional differences, industrial competitiveness, and new-type urbanization.

The increasing climate inequalities of urban carbon emissions: The distributional effect of low-carbon city pilot policy

The low-carbon city pilot (LCCP) policy has been implemented for >10 years in China. While previous studies have proved its effectiveness in facilitating low-carbon transformation, the distributional effects of the LCCP have not been fully explored to address the potential climate inequalities across cities. This study employs the changes-in-changes (CIC) model to explore the distributional effect of China's LCCP policy on carbon emission intensity (CEI). Our results show that the LCCP effectively reduces CEI, but its effect is uneven across cities and only lasts for the short term. In addition, the climate benefits of the LCCP diminish at higher quantiles of CEI, where urgent actions are required for CEI reduction. This unequal effect is driven by LCCP's regressive impact on productivity, leading to a widening gap among cities and undermining climate justice. This study raises the importance of equity concerns in climate policy effects. We call for policy efforts on regional coordination and fair distribution of climate responsibilities, technological transfers and financial assistance to complement climate policies.

THE IMPACT OF DIGITAL ECONOMY DEVELOPMENT ON CARBON EMISSION INTENSITY REDUCTION: EVIDENCE FROM CHINA

Digital economy development is important for promoting industrial upgrades and high-quality economic growth. As excessive carbon emissions are a threat to sustainable economic growth, whether digital economy development contributes to reducing carbon emission intensity has gained attention. This study employs the fixed effects model and spatial Dubin model to examine the effect of digital economy development in 275 cities in China on the reduction of carbon emissions intensity. The results show effectiveness in reducing carbon intensity with a spatial spillover effect to neighboring areas. Urban economic primacy plays a positive modulating role in carbon reduction. Policy implications are discussed.

New urbanization and carbon emissions intensity reduction: Mechanisms and spatial spillover effects

The advancement of new urbanization policy (NU) and the attainment of double carbon targets play pivotal roles in facilitating high-quality economic development in China. This paper conducts a comprehensive analysis of the mechanism and spatial spillover effects of NU on carbon emission intensity reduction (CEIR), building upon an examination of the nature of NU and the principles of urban carbon pollution control. The research employs a multi-period difference-in-difference model (DID) to explore the causal relationship between NU and CEIR, using panel data from 278 prefecture-level cities spanning the period of 2006 to 2020. Empirical results demonstrate that the implementation of NU resulted in an 8.4 % reduction in carbon emission intensity (CEI). Furthermore, the analysis of the transmission mechanism reveals that NU stimulates green technology innovation and facilitates the development of industrial agglomeration, thus achieving CEIR. The decomposition of the spatial Durbin model indicates significant spatial spillover effects in the effectiveness of NU, signifying its positive impacts not only within the region but also in generating benefits for surrounding areas. Moreover, the dynamic heterogeneity results indicate that entrepreneurial vitality and urbanization rate exhibit dynamic effects on the policy's CEIR effect, both displaying nonlinear enhancement curves. Based on this, the policy implications of this paper include: The government should enhance regional coordinated governance to address carbon emissions pollution in alignment with China's NU. This can be accomplished by effectively harnessing the driving role of green innovation and industrial agglomeration. Additionally, the local government can actively create an entrepreneurial atmosphere and expedite the urbanization process in order to support NU in the implementation and achievement of CEIR.

Development of a Factorial Hypothetical Extraction Model for Analyzing Socioeconomic Environmental Effects of Carbon Emission Intensity Reduction

China has pledged to peak its carbon emissions before 2030 and achieve the net-zero ambition in the 2060s. Reducing the national carbon emission intensity can help achieve these ambitions effectively. To explore the tradeoff between emission reduction and system health, a factorial hypothetical extraction method has been proposed. It was applied to identify key carbon emission sectors, and further help formulate countermeasures on reducing the national emission intensity. A seven-factor factorial analysis was developed to evaluate the effects of factors (i.e., 7 countermeasures) and the combinations (i.e., 128 scenarios) on responsive variables (i.e., system health). Main effects and interactions for response variables were also detected between these factors. Results show that the most effective combination, i.e., simultaneously enlarging the production scales of agriculture and other services, and lessening those of metallurgy sectors, can help reduce emission intensity by −19.2%. Lessening the production scales of electricity-generation/metallurgy, and enlarging those of wholesale and retailing sectors, can help reduce national emission intensity, while these factors negatively impacted system sustainability and robustness. The mitigation effects of these countermeasures will be weakened if these countermeasures are implemented simultaneously. Enlarging the production scales of leasing and commercial services and other services sectors can help achieve a win-win outcome.

Research on the Influence of Enterprise’s Digital Transformation on Carbon Emission Intensity——A Moderated Mediation Model

Digitalization and low-carbon development are parallel in the digital economy. In what ways does the development of low-carbon economy can be contributed by the digital transformation of enterprises? This paper analyzes the impact of digital transformation on carbon emissions from a micro perspective. Based on unbalanced panel data from 278 listed companies between 2010 and 2018, a double fixed-effect model is used to test the overall effects of digitalization on carbon emission intensity, the intermediary role of green technology innovation capacity and the regulatory effect of absorptive capacity. Results show that digital transformation of enterprises contributes to the reduction of carbon emission intensity, green technology innovation capability partially mediates the impact of digital transformation on carbon emission intensity, and absorptive capacity moderates the impact of digital transformation on green technology innovation capacity. Based above, this paper puts forward corresponding countermeasures from the aspects of digitalization, greening and low carbon.

Can digital economy reduce carbon emission intensity? Empirical evidence from China's smart city pilot policies.

With the rapid development of digital technology worldwide, can the digital economy promote not only macroeconomic growth but also green and low-carbon economic development? On the basis of the urban panel data in China from 2000 to 2019, this study uses a staggered difference-in-difference (DID) model to explore whether or how the digital economy reduces carbon emission intensity. Results show the following. (1) The development of digital economy is conducive to reducing the carbon emission intensity of local cities, and the above conclusion is relatively stable. (2) There is strong heterogeneity in the impact of digital economy development on carbon emission intensity in different regions and different types of cities. Chinese smart city construction in southern cities, non-resource-based cities, and environment protection-focused cities significantly reduces urban carbon emission intensity. (3) Mechanism analysis shows that the digital economy can promote the upgrading of the industrial structure, improve the efficiency of energy utilization, improve the efficiency of environmental regulation, reduce the mobility of the urban population, improve the environmental protection awareness of residents, promote the modernization of social services, and achieve emission reduction from the "production side" and "living side." (4) Further analysis shows the change of the influence between the two in the space-time dimension. In the spatial dimension, the development of digital economy can promote the reduction of carbon emission intensity in neighboring cities. In the time dimension, the early stage of digital economy development may increase the intensity of urban carbon emissions. The energy-intensive nature of digital infrastructure leads to lower energy utilization efficiency in cities, thus improving urban carbon emission intensity.

Does the Digital Economy Promote the Reduction of Urban Carbon Emission Intensity?

The impact of the digital economy is increasing, and its environmental effect has attracted more and more attention. The digital economy promotes the improvement of production efficiency and the government's environmental governance capacity, and contributes to the reduction of urban carbon emission intensity. In order to study the impact of digital economy development on urban carbon emission intensity, this paper analyzes the theoretical basis of the digital economy on the reduction of carbon emission intensity, and then, based on the panel data of cities from 2011 to 2019, uses the two-way fixed effect model for empirical testing. The regression results show that the development of the digital economy has promoted the reduction of carbon emission intensity of cities, promoted the green transformation and upgrading of cities, and lays a foundation for China to achieve carbon peaking and carbon neutralization through the improvement of human capital investment and green innovation level. The basic conclusion is robust by changing core explanatory variables, changing samples, replacing regression methods, and shrinking and truncating tests. The impact of the digital economy on urban carbon emission intensity varies with the location, grade and size of the city. Specifically, the development of the digital economy in cities in the eastern and central regions, cities at or above the sub provincial level, large cities and non-resource-based cities has promoted the reduction of urban carbon emission intensity. In terms of resource-based cities, the development of the digital economy in renewable resource-based cities and resource-based cities dominated by iron ore and oil mining has promoted the decline in urban carbon emission reduction intensity.

Study on Contribution of Carbon Emission Intensity Based on Oaxaca-blinder Decomposition Method by Big Data Evaluation in Different Economic Level Cities of Shandong Province

We studied the difference in carbon emission intensity by the Oaxaca blinder decomposition method with the panel data of 17 prefecture level cities in Shandong Province from 2007 to 2016. Per capita GDP, industrial structure, urbanization level, and energy consumption intensity are important factors affecting carbon emission intensity. The increase in urbanization rate and energy consumption intensity leads to an increase in carbon emission intensity. The reduction of the proportion of secondary industry in low-level cities is conducive to the reduction of carbon emission intensity. At the same time, we selected factors from the economic, energy, and social indicators in the low-level city rating system to reveal the contribution of different factors to the difference in carbon emission intensity. This is helpful to narrow the gap among the cities, accelerate the transformation from low-carbon cities to non-carbon cities, and realize the economic development of low-level cities.

Impact assessment and mechanism of water conservancy policy on carbon emission performance under the background of artificial intelligence

AbstractArtificial intelligence technology is constantly developing and changing. Due to the continuous development of technology and production of carbon emissions, various problems have occurred, forcing various regions to start promoting low‐carbon work. What kind of method can effectively and quickly improve the performance of carbon emissions has become one of the current research topics that has attracted much attention. In response to this problem, it is of great significance to study in the field of carbon emission performance improvement methods. With the in‐depth research on carbon emissions, the research on the intervention of water conservancy policies in carbon emissions has gradually been carried out, and its functional effects are of great significance to solve the problem of improving carbon emissions performance. This paper has aimed to study the impact and mechanism of water conservancy policy on carbon emission performance in the context of artificial intelligence. Through the analysis and research of the water conservancy policy under artificial intelligence, the double difference model (DID) is used to evaluate it, so that it can show the impact and mechanism on carbon emission performance, so as to solve the problem of improving carbon emission performance. This paper has analysed water policy, carbon emission performance and DID model under artificial intelligence. Its influence and mechanism have been experimentally analysed, and related theoretical formulas have been used to explain. The results have shown that the water conservancy policy has a significant and continuous driving effect on the reduction of carbon emission intensity, and its interaction coefficient under the control variable is −0.934, which is significantly negative. Further analysis of variables has shown that the energy intensity coefficient is 0.714, and the secondary industry coefficient is 0.924, which are both positive and significant. Water conservancy policies mainly reduce carbon intensity through the improvement of energy efficiency and industrial structure upgrading. It can be seen that water conservancy policies under artificial intelligence can meet the needs of improving carbon emission performance, and the level of economic growth and green development has been greatly improved.

Carbon emission quota allocation of high energy consumption industries in undeveloped areas – A case study of Inner Mongolia Autonomous Region

With the approaching of the target year of carbon peak, all world countries have gradually strengthened carbon emission reduction actions. However, most of the undeveloped countries or regions do not belong to the Annex I countries in the Kyoto Protocol, and the development of carbon emission trading mechanisms is relatively slow. Therefore, setting emission quota standards for industries in underdeveloped regions is necessary. Considering the principles of fairness, efficiency, and sustainability, we establish a “multi-objective information entropy allocation model” (i.e., IEMMA method) for underdeveloped areas. Take China's Inner Mongolia Autonomous Region as an example, and the allocation results show that: (1) The “historical emissions”, “carbon emission intensity”, “cumulative emissions”, “energy structure”, and “energy intensity” are assigned higher weights. Such industries will carry a higher responsibility for emissions reductions, they should gradually transfer emission quotas to industries with smaller historical emissions to increase enthusiasm for reducing emissions. (2) Compared with the historical emissions allocation scheme, the IEMMA model can increase the emission limits for industries with large historical emissions. For industries with relatively small emissions, their emission quotas can be appropriately relaxed to promote the sustainable development of the industry. (3) Under the allocation scheme of the IEMMA model, the difference in the reduction of carbon emission intensity of different industries is small. Carbon emission intensity becomes a relative emission reduction indicator, which reflects the fairness of allocation while maintaining economic development. (4) The “quota gap” formed by the allocation plan of the IEMMA model is large, which is conducive to promoting the active emission reduction of high-emission enterprises. Larger “allowance gaps” are conducive to increasing the activity of the carbon trading market. In summary, the IEMMA model is conducive to achieving long-term emission reduction goals in underdeveloped regions and is more suitable for the allocation of quotas to high energy-consuming industries.

Does improvement of environmental efficiency matter in reducing carbon emission intensity? Fresh evidence from 283 prefecture-level cities in China

While promoting environmental efficiency is widely perceived as one of the panaceas for solving environmental issues in China, little effort has been made to identify whether environmental efficiency improvement helps to reduce carbon emission intensity (CEI). Therefore, the work presented in this study remedies the inadequacies in the previous literature by investigating the linkages between environmental efficiency and CEI, by explicitly considering its spatiotemporal heterogeneous effect and dynamic measurement. We first measure the environmental efficiency (EE) and its growth (environmental total factor productivity) at prefecture level in China during 2003–2016 by incorporating a recently modified epsilon-based data envelopment analysis technique in the global Malmquist–Luenberger index. A spatial panel regression approach is then applied to reveal the impact mechanism of EE and its growth on the CEI. The empirical results confirm that promoting EE and environmental technology efficiency can help mitigate the urban CEI. However, the impact of economic scale efficiency on the CEI reduction has not been verified, indicating great room for CEI mitigation by optimizing the urban production scale and reducing production redundancy. Evidence from the spatiotemporal econometric analysis shows a spatially heterogeneous effect of EE on CEI, and the strength of influence tends to decrease over time. The findings also reveal that enhancing EE matters in reducing CEI for southern and western cities. Finally, several control variables have negative direct and indirect (spillover effect) effects on CEI reduction, including urbanization, industrial structure, and foreign direct investment. Our findings suggest that strengthening the synergistic effect between environmental pollution governance and carbon emission control by enhancing EE to promote low-carbon development in Chinese cities.

Does green innovation suppress carbon emission intensity? New evidence from China.

The sustainable development of human beings is facing severe challenges in the context of a new era. The effective reduction of carbon emission intensity is essential to achieve the goal of sustainable development. Obviously, green innovation is an important factor in mitigating carbon emission intensity. It is important to measure the effect of green innovation on carbon emission intensity for accelerating industrial transformation and building a circular economy system. Therefore, this paper uses the data of 30 provinces in China from 2000 to 2019, obtained by the State Intellectual Property Office, using fixed effects model quantile regression model and Spatial Durbin Model to empirically verify the theoretical hypothesis. The conclusions are as follows: (1) Green innovation inhibits carbon emission intensity. Instrumental variable model and robustness test support this conclusion. (2) For carbon emission intensity under different quantiles, green innovation has a more significant effect on provinces with high carbon emission intensity through "target accountability system" and "reverse coercive system." (3) There is a significant spatial correlation between green innovation in China's provinces. The reduction of carbon emission intensity in the region will benefit from the improvement of green innovation in surrounding cities.

Research on the Heterogeneity of Carbon Emissions under the Government’s Promotion of Urban Agglomeration Development: Empirical Evidence from County-to-District Reforms

County-to-district reform (CTDR) is an important policy path for the government to promote the cultivation and construction of urban agglomerations, and exploring its “carbon emission” effect is of great significance for the high-quality development of urban agglomerations and the realization of the “dual carbon” goal. Based on the panel data of 120 counties in the Yangtze River Delta urban agglomeration from 2000–2017, this paper empirically tests the effect of county-to-district reforms on per capita carbon emissions in the counties of the central and peripheral cities of the Yangtze River Delta urban agglomeration under the Kutznets curve (EKC) hypothesis and the integrated difference-in-difference (DID) model and STIRPAT model. The results show that: (1) The carbon emission effect of county-to-district reforms have significant regional heterogeneity. The reforms of the central city of the urban agglomeration significantly reduced the per capita carbon emission of the county by 4.27%, whereas the reforms of the periphery cities of the urban agglomeration significantly increased per capita carbon emission by 6.56%. (2) The impact of county-to-district reforms on county per capita carbon emissions began to appear in the fourth year of reform. (3) Mechanism analysis showed that county-to-district reforms promoted central cities population agglomeration and reduction of carbon emission intensity can help reduce the per capita carbon emission level in counties, whereas peripheral cities have a dual carbon-increasing effect of decreasing population density and increasing carbon emission intensity. Therefore, the approval of county-to-district reforms should be strictly controlled, and the reform of non-central cities would be especially prudent, so as to reduce the negative effect of reform on the high-quality development of cities.

Effects of Carbon Trading Pilot on Carbon Emission Reduction: Evidence from China’s 283 Prefecture-Level Cities

Empirical evidence demonstrates that market-driven carbon trading scheme (ETS) is a crucial instrument for China to control environmental pollution. Based on the panel data of China’s 283 prefecture-level cities from 2006 to 2017, this research investigated the transmission mechanism, direct and indirect effects of ETS on carbon emission intensity (CEI) using difference-in-differences (DID) model, propensity-score-matched difference-in-differences (PSM-DID) model at national, regional, and local levels (cities with different industrial characteristics). The results demonstrated the mediating effects of total energy consumption, energy consumption structure, and industrial structure upgrading in the incentive role of ETS on CEI reduction. Moreover, ETS directly and effectively reduced CEI at the national level, while the spatial heterogenous effects were identified at regional and local levels, which emphasises the necessity and importance of unified carbon trading market establishment and classified governance.

Decomposition and prediction of China's carbon emission intensity towards carbon neutrality: From perspectives of national, regional and sectoral level

China has actively participated in global climate governance and put forward its ambitious carbon neutrality target. The formulation of scientific plans has become the primary concern of the policy makers, especially for the 14th and 15th Five-Year Plans (FYP) which are important periods to secure the neutrality pledge and transform the whole economy. Since the carbon emission intensity play a key role in achieving carbon neutrality, it is necessary to summarize and explore the evolution trend of carbon emission intensity as well as its driving factors. Therefore, an integrated decomposition framework is developed to study the carbon emission intensity in the past three FYPs from the national, regional and industrial levels. Furthermore, towards the carbon neutrality target, moderate scenario and advanced scenario are designed to predict the future evolution trend of the carbon emission intensity and driving factors in the 14th and 15th FYPs (2021–2030). The main results are as follows: (1) During the three FYPs, factor substitution is the main force contributing to the decreased carbon emission intensity, but this effect gradually decreased. This indicates that it is an inevitable trend to further promote internal optimization and reform of energy system. (2) The change of energy structure exerts a positive effect on the carbon emission intensity decline, but it is not significant, especially in the industrial sector. (3) With the rich factor endowment, central and eastern regions can reduce carbon emission intensity through factor substitution and industrial structure transformation, while the western region is not. (4) In the future, the role of industrial structure optimization and technology progress will be gradually significant. Finally, our findings provide practical guidance on achieving carbon emission intensity reduction and enlightenments on policymaking.

Potential assessment of photovoltaic power generation in China

Accurate assessment of the photovoltaic (PV) power generation potential in China is important for the reduction of carbon emission intensity and the achievement of the goal of Carbon Neutral. This study used a PV power generation potential assessment system based on Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods to investigate the PV power generation potential in China. Firstly, the high spatial-temporal resolution and high-quality ERA5 data and related technical, geographic, and social factors were used to assess the theoretical power generation and land suitability of PV power generation. Then, the theoretical power generation and land suitability were comprehensively considered to evaluate the PV power generation potential of China in 2015. The results showed that the average suitability score of land in China is 0.1058 and the suitable land for PV power generation is about 993,000 km2 in 2015. The PV power generation potential of China is 131.942 PWh, which is approximately 23 times the electricity demand of China in 2015. The spatial distribution characteristics of PV power generation potential mainly showed a downward trend from northwest to southeast. Meanwhile, there were clear spatial dislocations between the PV power generation potential and the population distribution and electricity demand in China. In areas that accounting for about 75% of the PV potential, population and electricity demand only accounted for about 16% of the total population and total electricity demand in China. Besides, the degree of tapping PV potential in China is not high, and the installed capacity of most provinces in China accounted for no more than 1% of the capacity potential, especially in the PV potential-rich areas.