Determinants of fishing grounds footprint: Evidence from dynamic spatial Durbin model

Despite a growing literature on fishing grounds footprint, there is no study analyzing fishing footprint regarding spatial effects between neighboring countries. Thus, we explored whether the fishing grounds footprint of 156 countries is spatially correlated. For this purpose, we applied the dynamic spatial Durbin model to examine the direct and indirect effects of GDP per capita, biological capacity, trade openness, population, and urbanization on fishing grounds footprint in the short-term and the long-term during 2001-2021. The results revealed that: (1) there exists a positive and significant spatial dependence in fishing grounds footprint between countries; (2) inverted U-shaped environmental Kuznets curve hypothesis is valid in the short-term and the long-term; (3) fishing grounds footprint is negatively influenced by biocapacity and urbanization in neighboring countries, while population directly increases the fishing footprint. Finally, some suggestions were put forward to reduce fishing grounds footprint and to achieve a sustainable fisheries environment.

Pollution load estimation and influencing factor analysis in the Tuhai River Basin in Shandong Province of China based on improved output coefficient method

Estimating the pollution loads in the Tuhai River is essential for developing a water quality standard scheme. This study utilized the improved output coefficient method to estimate the total pollution loads in the river basin while analyzing the influencing factors based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Findings indicated that the projected point source pollution loads for total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (AN) would amount to 3937.22 ton, 335,523.25 ton, and 13,946.92 ton in 2021, respectively. Among these, COD pollution would pose the greatest concern. The primary contributors to the pollution loads were rural scattered life, large-scale livestock and poultry breeding, and surface runoff. Per capita GDP emerged as the most influential factor affecting the pollution loads, followed by cultivated land area, while the urbanization rate demonstrated the least impact.

Assessing the impact of incentive coordination effect on the equilibrium of agricultural water usage by China's South-to-North Water Diversion Middle Route Project

In recent years, the sustainable development of agricultural water resources has received much attention. The mismatch between agricultural water distribution patterns, land resources, and socioeconomics threatens food production, especially in vast water-scarce plains. Therefore, monitoring agricultural water spatial equilibrium (AWRSE) is necessary. Based on equilibrium theory and information entropy, in this study, the AWRSE evaluation model is constructed from three aspects: agricultural water resources, land resources, and socioeconomics. In addition, the relationship between social factors with cropping pattern as the primary explanatory variable and AWRSE was examined in conjunction with the extended STIRPAT model and applied to the water-receiving area of the Middle Route of South-to-North Water Diversion Project (MR-SNWDP). The results show that compared with the pre-diversion period, the AWRSE of 75% of the water-receiving cities has been significantly improved by the MR-SNWTP water supply. The MK test z value of the overall regional AWRSE has changed from - 0.328 to - 2.65, and the AWRSE development has shifted from not significantly better to significantly better. The cropping pattern shows a positive response to this development, and this effect can be mitigated in the late stage of water transfer; when the proportion of food crop cultivation increases by 1%, the sub-regional AWRSE value will increase by 0.347%. The evaluation model demonstrates a broad range of inclusiveness and application potential; it provides novel insights for examining agroecological, social, and economic stability.

Distribution characteristics and influencing factors of household consumption carbon emissions in China from a spatial perspective

Household consumption carbon emissions (HCCEs) have become the main growth point of China's carbon emissions in the future. It is important to investigate the factors affecting the demand-side carbon emissions in order to find the accurate entry point of emission reduction and achieve carbon peaking and carbon neutrality goals. Different from previous studies, this study analyzed the spatial and temporal evolution characteristics of provincial HCCEs in China from a spatial perspective by using the Theil index and spatial auto-correlation and explored the key influencing factors and spatial spillover effects of HCCEs in different regions by using an econometric model. The results of the study showed that: (1) Per capita HCCEs increased by 11.90% annually, and the eastern region > northeastern region > western region > central region. (2) There were regional differences in per capita HCCEs, but the decrease was significant at 40.32%. (3) The spatial agglomeration effect of per capita HCCEs was significant, and the hot spots were mainly concentrated in the eastern coastal areas. (4) From the national level, every 1% increase in residents' consumption power would increase HCCEs by 2.489%. Which was the main factor for the growth of HCCEs, while the increase in fixed asset investment would restrain HCCEs. At the regional level, the change in population size significantly increased the HCCEs in the eastern and central regions. While for the western region, a 1% increase in population would reduce the HCCEs by 0.542%. For the eastern and central regions, the degree of aging and the consumption structure of residents could suppress regional HCCEs. However, the consumption structure of residents drove the growth of HCCEs in the western region. For the Northeast region, residents' consumption capacity and cooling degree days were the main factors for the growth of residents' consumption, while fixed asset investment could inhibit the growth of HCCEs.

An analysis and prediction of carbon emissions in the sphere of consumer lifestyles in Beijing

In alignment with China's "dual carbon" goals and its quest to build an ecological civilization, this study scrutinizes the carbon emissions derived from consumer lifestyles, with a particular focus on Beijing, a high-consumption urban metropolis. Utilizing the expanded STIRPAT model and ridge regression, factors such as permanent population, per capita consumption expenditure, energy intensity, energy structure, and consumption structure are examined to evaluate their impact on lifestyle-associated carbon emissions. A scenario analysis is also conducted to project future carbon emissions in Beijing. From 2010 to 2020, there was an overall upward trend in lifestyle-associated carbon emissions, up to a maximum of 87.8260 million tons. Indirect consumption-related carbon emissions, particularly those associated with residential and transportation-related consumption, constituted the primary sources. The most influential factors on carbon emissions were found to be the consumption structure. Notably, adopting a low-carbon consumption mindset and an optimized consumption structure could foster significant carbon reduction. Projections suggest that by 2035, carbon emissions due to residents' consumption could decline by 39.72% under a low-carbon consumption scenario and by 48.74% under a coordinated development scenario. Future efforts should prioritize promoting green, low-carbon living, refining consumption structure and practices, curbing excessive housing consumption, improving energy structure, and raising technological and energy efficiency standards.

The impact of population characteristics on transportation CO2 emissions-does population aging important?

Reducing transportation CO2 emissions and addressing population characteristic changes are two major challenges facing China, involving various requirements for sustainable economic development. Due to the interdependence of population characteristics and transportation, human activities have become a significant cause of the increase in greenhouse gas levels. Previous studies mainly focused on evaluating the relationship between one-dimensional or multi-dimensional demographic factors and CO2 emissions, while few studies have reported on the effect of multi-dimensional demographic factors on CO2 emissions in transportation. Analyzing the relationship between transportation CO2 emissions is the foundation and key to understanding and reducing overall CO2 emissions. Therefore, this paper used the STIRPAT model and panel data from 2000 to 2019 to investigate the effect of population characteristics on CO2 emissions of China's transportation sector, and further analyzed the effect mechanism and emission effect of population aging on transportation CO2 emissions. The results show that (1) population aging and population quality restrained CO2 emissions from transportation, but the negative effects of population aging were indirectly caused by economic growth and transportation demand. And with the aggravation of population aging, the influence on transport CO2 emissions changed and presented a U-shape. (2) Population living standard on transportation CO2 emissions exhibited an urban-rural difference, and urban living standard was predominant in transportation CO2 emissions. Additionally, population growth is under a weakly positive effect on transportation CO2 emissions. (3) At the regional level, the effect of population aging on transportation CO2 emissions showed regional differences. In the eastern region, the CO2 emission coefficient of transportation was 0.0378, but not significant. In central and western regions, the influence coefficient of transportation was 0.6539 and 0.2760, respectively. These findings indicated that policy makers should make relevant recommendations from the perspective of coordinating population policy and energy conservation and emission reduction policy in transportation.

A Taguchi-STIRPAT input-output model for unveiling the pathways of reducing household carbon emissions under dual-carbon target-A case study of Fujian province

This study develops a novel Taguchi-STIRPAT input-output (TSIO) model for exploring pathways to reduce carbon emission from the perspective of household consumption, through incorporating input-output model (IOM), Taguchi design (TD), and STIRPAT model. TSIO can not only identify the main factors (carbon emission intensity, consumption structure, per capita consumption, and population) and evaluate their effects on indirect household carbon emissions (IHC), but also predict IHC from a long-term perspective to achieve the dual-carbon target. TSIO is then applied to Fujian province (China), where multiple scenarios related to multiple factors with multiple levels are examined. Results reveal that (i) among all sectors, the highest contributors to IHC are residence (RES), followed by food, cigarettes, and drinks (FCD), and transport and communication (TC); it is suggested that the government can consider market mechanism to control these high-carbon emission consumption behaviors; (ii) the decline in the share of RES consumption has the largest effect on rural and urban IHC; the share of RES consumption is considered to be a key factor in predicting carbon emissions; (iii) under the optimal scenario, IHC would peak in 2025 and decrease to 10.07 × 106 ton in 2060; this scenario can effectively mitigate household carbon emissions by reducing carbon emission intensity and the share of RES consumption; at the same time, it can ensure a sustained increase in per capita consumption. The results unveil the pathways of household carbon reduction under the dual-carbon target in Fujian province and suggest the local government should adopt policies (such as taxation and financial incentives) to limit residential consumptions with high carbon emission intensity.

Study on carbon emission reduction countermeasures based on carbon emission influencing factors and trends

The carbon mitigation response encompasses a variety of strategies aimed at mitigating greenhouse gas emissions resulting from human activities. These measures are crafted to address the challenges posed by climate change and facilitate the transition of businesses towards a low-carbon paradigm. Leveraging the analytical outcomes of the extended STIRPAT model and the PSO-BP prediction model, this paper suggests countermeasures for reducing carbon emissions in China's metal smelting industry. The overarching objective is to contribute to China's attainment of the "dual carbon objectives." The study identifies key factors influencing carbon emissions in the metal smelting industry, ranked in descending order of sensitivity: population, coal consumption, urbanization rate, total metal production, carbon intensity, proportion of secondary industry, and GDP per capita. Results from three established scenarios-namely, low carbon, standard, and high carbon-indicate a consistent decline in carbon emissions from China's metal smelting industry over the next 15 years. This research not only enhances the findings of existing studies on carbon emissions in the metal smelting sector but also introduces an innovative approach to carbon emission reduction within China's metal smelting industry.

Impact of artificial intelligence on carbon emission efficiency: evidence from China

Artificial intelligence (AI) has been extensively used as a revolutionary and versatile technology in various fields. However, scholars have not given substantial consideration to the impact of AI on the environment, particularly carbon emission efficiency (CEE). This study adopts the global super-efficiency slacks-based model to evaluate CEE of 30 provinces in China from 2006 to 2019. Thereafter, the current study investigates the impact mechanism of AI on CEE using the stochastic impact of population, affluence, and technology (STIRPAT) model. The empirical analysis provides the following valuable research findings. First, AI, represented by industrial robots, can significantly improve CEE. Second, AI can enhance CEE by promoting technological innovation and upgrading industrial structures. Lastly, the relationship between AI and CEE is influenced by marketization and government intervention.

Impact of the intensive use of urban construction land on carbon emission efficiency: evidence from the urban agglomeration in the middle reaches of the Yangtze River

Urban construction land, as the main carrier of socioeconomic activities, is also a land type that is associated with large carbon emissions. This study uses statistical data of the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) from 2006 to 2020 to examine the mechanism of the intensive use of urban construction land (IUUCL) on carbon emission efficiency (CEE) from the perspective of urban land resource utilization. The study shows that the capital-intensive and technology-intensive use of urban construction land can significantly increase CEE, while increased labor and energy intensification inhibits CEE. In addition, there is regional heterogeneity in the effect of the IUUCL on CEE. The external control factor industrial structure has the most obvious inhibiting effect on the CEE of the Wuhan urban circle, the intensive use of energy has become the crucial constraint on the carbon emission reduction of the city cluster around Poyang Lake, and the intensive use of science and technology is the key factor in realizing the green and low-carbon development of the Chang-Zhu-Tan city cluster. This study innovatively constructs a theoretical framework of IUUCL versus CEE and conducts a heterogeneous study on the CEE of intensive use of construction land from the perspective of urban agglomerations. By providing a better understanding of the intrinsic influence mechanism of both these processes, this study provides a new perspective for reducing carbon emissions.

Democracy, green energy, trade, and environmental progress in South Asia: Advanced quantile regression perspective

Unquestionably, the industrial revolution of the twenty-first century contributes to global warming. Excessive amounts of carbon emissions into the atmosphere are responsible for global warming. Therefore, this research aims to assess the impact of GDP, green energy consumption, population, trade openness, and democracy on CO2 emissions in four selected South Asian countries from 1990 to 2019. This research also attempts to evaluate the EKC hypothesis in terms of economic growth (GDP2). The unit root of panel data and cointegration tests are executed in this study as a prelude to the regression analysis. Quantile regression for panel data, which (Powell, 2016) devised to deal with the fixed effect problem, is used in this study, and (Powell, 2016) empirical findings are the main focus. The estimated coefficient of GDP is positively significant, demonstrating that economic activity increases the burning of fossil fuels and upsurges atmospheric CO2 emissions. After attaining economic development, the reversed U-shaped EKC theory is valid for four selected South Asian countries. Economic development encourages these countries to use green technology, which helps mitigate CO2 emissions. The research, however, reveals that green energy is to blame for CO2 emissions. Burning biomass releases carbon dioxide that negatively impacts the quality of the environment. The study confirms that human activities are the leading contributor to environmental deterioration. Population growth has a worsening effect on the environment. The association between population and CO2 emissions is positively significant. The estimated coefficient of trade openness is positive, which increases CO2 emissions significantly. The estimated coefficient of democracy is quite negative. Therefore, this study suggests prioritizing democracy to reduce CO2 emissions. Citizens who live in democracies are better informed, more organized, and able to protest, all of which contribute to increased government responsiveness to environmental preservation. The results of the Wald test support the differential effects at various quantiles. The Dumitrescu-Hurlin (2012) panel causality tests are also used in this analysis to check causality between variables. Based on the findings, this research makes many policy suggestions for lowering carbon emissions.

Does Economic Policy Uncertainty Cause Environmental Pollution? Fresh Evidence From Developed Countries

The industrial revolution has dramatically altered the environment and ecosystem. So many scholars have empirically attempted to reveal the most influential anthropogenic factors on environmental degradation. For this purpose, this study examines the leading determinants of CO2 emissions in the context of economic policy uncertainty (EPU) for 14 developed countries within the framework of the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) environmental model from 1997-2018. For empirical modeling, CO2 emission is treated as the dependent variable, which is a strong proxy for environmental degradation. In addition to the GDP per capita, population density, and energy intensity (a proxy for technology), the basic model is extended to include variables such as EPU, renewable energy, trade openness, globalization, and information and communications technology (ICT) index. While the estimation results by the dynamic conditional correlation (DCC) estimator, which are also supported by robustness analysis, suggest that GDP per capita and energy intensity are the main contributors to emission levels, population density has no significant impact on CO2. Furthermore, while renewable energy (in model 2), trade openness (in model 4), and globalization (in model 6) have negative impacts on CO2 emission, technology (in models 5 and 6) and EPU (in model 6) make marginal contributions to CO2.

[Characteristics and Driving Factors of O3 Pollution During 13th Five-Year Period in Tianjin]

The spatial distribution, accumulation features, and driving factors of O3 pollution were analyzed using spatial autocorrelation and hotspot analysis and the STIRPAT model based on the high spatiotemporal resolution online monitoring data from 2016 to 2020 in Tianjin. The results showed that the variation characteristics of O3 concentration in Tianjin from 2016 to 2020 had the trend of pollution occurring in advance and the scope of the pollution expanding. The distribution of O3 pollution showed significant aggregation from June to October. High-high value clustering areas included six urban districts, Beichen District, Jinnan District, and Jinghai District. O3 concentration formed high value hot spots in the southwest and low value cold spots in the northeast. Meteorological factors such as temperature, breeze percentage, and sunshine duration, as well as social factors such as NOx emission, VOCs emission, and motor vehicle ownership had significant effects on O3 concentration. The regression fitting effect of the integrated drive STIRPAT model was better than that of the single meteorological factor or social factor models. In order to promote scientific and efficient prevention and control of ozone pollution during the 14th Five-Year Plan period, meteorological conditions require attention; under the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," it is necessary for Tianjin to further improve the emission performance of steel, petrochemicals, thermal power, building materials, and other industries, Additionally, clean upgrading, transformation, and green development should be guided for enterprises to reduce VOCs and NOx emissions. At same time, the increase in fuel vehicle numbers should be controlled, and new energy vehicles should be vigorously promoted to reduce vehicle emissions.

Unveiling the liaison between financial development dimensions, energy efficiency and ecological footprint in the context of institutional frameworks: evidence from the Emerging-7 economies

Financial development and energy efficiency can facilitate the transition towards a more environmentally sustainable and responsible economy. Simultaneously, the importance of institutional effectiveness cannot undermine the need to manage financial and energy consumption activities. To this end, the primary objective of this study is to examine the effects of financial development and energy efficiency on the ecological footprint of the Emerging-7 economies from 2000 to 2019. The study specifically focuses on the influence of these factors within the context of robust institutional mechanisms. We employ the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model as the analytical framework to accomplish this. This study takes into consideration three aspects of financial development, which are: (i) the depth of financial development, (ii) the stability of financial development, and (iii) the efficiency of financial development. In addition, this study has developed an institutional index using principal component analysis. The index comprises several crucial indicators: Control of Corruption, Government Effectiveness, Political Stability, Regulatory Quality, Rule of Law, and Voice and Accountability. The study raises the importance of energy efficiency in terms of energy intensity on ecological footprint. The study's findings suggest that without robust institutional mechanisms, the potential of financial development depth, stability, and efficiency to improve ecological well-being may not be fully realized. However, the study concludes that these institutional mechanisms positively impact mitigating the ecological footprint.

Green transition and economic growth in G20 countries: evidence from disaggregated energy sources

Green transitioning through renewable energy sources is the most effective strategy for any economy. This study investigates the extent to which G20 countries are shifting towards a green economy compared to prioritizing economic growth. To this end, the present study analyzes the nodes between income and renewable (solar, wind, hydro, and biomass) and nonrenewable (oil, coal, and gas) energy sources for the period of (1997-2020) in G20 countries. The energy-environmental Kuznets curve method is applied to study their behavior at various stages of growth. The main findings showed that wind, solar, and biomass energies have an inverted N-shaped relationship with income. The hydroelectricity did not follow any traditional EKC shape, showing a steady positive trend and growth. While nonrenewable energy consumption, i.e., coal, oil, and gas, follows an N-shaped EKC curve. The impact of foreign direct investment in the solar and wind sectors is positive. The varying outcomes concerning foreign direct investment (FDI) indicate that although G20 countries strive to achieve their green transition objectives by discouraging environmentally harmful investments, their success remains limited. The study indicates that G20 nations are progressing toward a green transition; however, additional technological innovations are required to transform these economies from brown to green. Governments can establish research institutions, offer grants and incentives, and encourage collaboration between academia, industry, and government to support green technology R&D.

The relationship between agricultural and animal husbandry economic development and carbon emissions in Henan Province, the analysis of factors affecting carbon emissions, and carbon emissions prediction

This study aims to investigate the relationship between agricultural and animal husbandry economic development and carbon emissions and the influencing factors on carbon emissions. Here, we combine the Tapio decoupling model with the STIRPAT model by using the panel data of Henan province from 2000 to 2020 for it. Our results reveal that (i) the main relationship between agricultural and animal husbandry economic development and carbon emissions is strong decoupling and weak decoupling; (ii) the intensity of carbon emissions and labor effects can optimize their relationship; (iii) the urbanization rate and per capita consumption expenditure in rural areas have a negative impact on carbon emissions, while the carbon emission intensity and total power of agricultural machinery are opposite. Therefore, Henan province needs to optimize its industrial structure, improve the economic level of rural areas, and reduce the use of fertilizers.

Evaluating a pathway for environmental sustainability: the role of energy mix and research and development in European countries

Within the European Union (EU), the majority of countries are considered developed, and the level of economic activity is rising. As a result, carbon dioxide emissions have increased. If the European Union wants to maintain long-term, sustainable growth, it must act quickly to find solutions to pollution. Population, wealth, renewable energy, nuclear energy, and research and development (R&D) are all factored into the STIRPAT model to determine their respective environmental impacts. Slope heterogeneity and cross-sectional dependence are explored in panel data for 30 European nations from 1990 to 2021 using a newly developed Cross Section Autoregressive Distributed Lag (CS-ARDL) method. The study found that population growth and the continued use of fossil fuels are major causes of environmental degradation. Alternately, employing renewable and raising incomes both have the potential to significantly cut pollution over the long run. Likewise, investments in R&D assist lessen the damage done to the environment. The nuclear energy coefficients, however, are insignificant. However, fossil fuels have negative effects on the ecosystem. If the EU wishes to stop the degradation of the environment, the analysis demonstrates that renewable energy is the best way to do it. The time has come for the EU to make a gradual transition away from fossil fuels and toward more environmentally friendly alternatives. Economic growth should be matched by decreased CO₂ emissions, and increasing investment in R&D can serve as a catalyst for environmental sustainability. The results were reviewed using three different estimators: the augmented mean group (AMG), the mean group (MG), and the common correlated effects mean group (CCEMG). Important policy recommendations for a sustainable European environment are also suggested by the research.

Examining the role of ICT, transportation energy consumption, and urbanization in CO2 emissions in Asia: a threshold analysis

ICT is viewed in earlier research as a double-edged sword that may either help or hurt the environment. Asian nations' ICT penetration has significantly expanded in recent years, and they are eager to bring about a digital revolution by building up their ICT infrastructure while consuming less energy for transportation and urban growth. Therefore, the purpose of this article is to investigate how ICT might reduce CO₂ emissions through the use of transport energy and urban development. Empirical and theoretical debates have been remaining ambiguous and contentious topic of whether energy consumed by the transport sector and urbanization causes CO₂ emanation in Asia, and what role ICT played in determining the level of CO₂ remains unanswered. This study adds to the ongoing discussion for sustainable transportation in ten Asian nations for 30 years that concentrate on the relationship between the energy consumption of transport, urbanization, ICT, and carbon emanation (1990-2020) and checked the validity of EKC. The STIRPAT and panel threshold models having two regimes are used to explore the stochastic impacts of the dependent and explanatory variables. We have divided explanatory into two categories, that is, the threshold variable ICT and the regime-dependent variables urbanization and transport energy consumption. Our results confirm that the EKC hypothesis holds in these Asian economies. Thus, our findings indicate that the environmental quality improves in terms of reduction in CO₂ emissions when ICT passes the threshold level due to the technological advancement in ICT dominating the scale effect induced by ICT. Furthermore, the possible policy recommendations are discussed according to the findings.

Driving forces and relationship between air pollution and economic growth based on EKC hypothesis and STIRPAT model: evidence from Henan Province, China

The aim of this research is to analyze the main influencing factors and relationship between atmospheric environment and economic society. Using the panel data of 18 cities in Henan Province from 2006 to 2020, this paper employed some advanced econometric estimation included entropy method, extended environmental Kuznets curve (EKC) and STIRPAT model to conduct empirical estimations. The results show that most regions in Henan Province have verified the existence of the EKC hypothesis; and the peak of air pollution level in all cities of Henan Province generally occurred in around 2014. Multiple linear Ridge regression indicated that the positive driving forces of air pollution in most cities in Henan Province are industrial structure and population size; the negative driving forces are urbanization level, technical level and greening degree. Finally, we used the grey GM (1, 1) model to predict the atmospheric environment of Henan Province in 2025, 2030, 2035 and 2040. What should pay close attention to is that air pollution levels in northeastern and central Henan Province will continue to remain high.

Carbon peak forecast and low carbon policy choice of transportation industry in China: scenario prediction based on STIRPAT model

As the second largest CO₂ emission department, transportation industry's carbon peak and carbon reduction are very important for China to smoothly achieve carbon peak by 2030 and carbon neutrality by 2060. This paper analyzes the influencing factors from the perspectives of population, economy, technology and transportation equipment structure, subdivides 20 scenarios to predict the carbon emissions of the transportation industry of the whole China and various regions based on scenario analysis method, explores the carbon peak path, and puts forward corresponding policy recommendations. The study found that (1) from the overall trend of carbon emissions, the total carbon emissions of China's transportation industry showed an overall upward trend from 2010 to 2019 while the growth rate of carbon emissions showed a downward trend. (2) From the perspective of influencing factors, population size, urbanization rate, economic scale, traffic development, traffic carbon intensity, and highway mileage have positive effect on the growth rate of China's transportation CO₂ emissions. The increase in the proportion of energy structure and railway cargo turnover has the negative effect on carbon emissions in the transportation industry. (3) From the prediction results at the national level, technological breakthroughs have a limited effect on carbon emission reduction in China's transportation industry, while structural equipment optimization has the most significant effect on its emission reduction. When technological breakthroughs and equipment structure optimization are carried out simultaneously, the carbon emission reduction effect is the best. The carbon peak of China's transportation industry would achieve as early as 2030, with a peak range of 70,355.54-84,136.17 million tons. (4) From the perspective of prediction results at the regional level, the provinces with rapid population growth and per capita GDP growth, the provinces with rapid population growth and per capita GDP growth, and the provinces with low population growth and per capita GDP growth should control their average annual growth rate of carbon emissions of the transportation industry to 1.13%, 0.72% and 0.58% respectively in 2019-2030, in order to ensure the achievements of the carbon peak target.

Regional allocation of carbon emission quotas in China under the total control target

The allocation of provincial carbon emission quotas under total amount control is an effective way for China to achieve its carbon peak and neutrality targets. Firstly, in order to study the factors influencing China's carbon emissions, the expanded STIRPAT model was constructed; and combined with the scenario analysis method, the total of national carbon emission quota under the peak scenario was predicted. Then, the index system of regional carbon quota allocation is constructed based on the principles of equity, efficiency, feasibility, and sustainability; and the allocation weight is determined by the grey correlation analysis method. Finally, the total carbon emission quota under the peak scenario is distributed in 30 provinces of China, and the future carbon emission space is also analyzed. The results show that: (1) only under the low-carbon development scenario, can China reach the peak target by 2030, with a peak carbon of about 14,080.31 million tons; (2) under the comprehensive allocation principle, China's provincial carbon quota allocation is characterized by high levels in the west and low in the east. Among them, Shanghai and Jiangsu receive fewer quotas, while Yunnan, Guangxi, and Guizhou receive more; and (3) the future carbon emission space for the entire country is modestly surplus, with regional variations. Whereas Hainan, Yunnan, and Guangxi have surpluses, Shandong, Inner Mongolia, and Liaoning have significant deficits.

Decoupling relationship between carbon emissions and economic development and prediction of carbon emissions in Henan Province: based on Tapio method and STIRPAT model

In order to cope with global warming, China has put forward the "30 · 60" plan. We take Henan Province as an example to explore the accessibility of the plan. Tapio decoupling model is used to discuss the relationship between carbon emissions and economy in Henan Province. The influence factors of carbon emissions in Henan Province were studied by using STIRPAT extended model and ridge regression method, and the carbon emission prediction equation was obtained. On this basis, the standard development scenario, low-carbon development scenario, and high-speed development scenario are set according to the economic development model to analyze and predict the carbon emissions of Henan Province from 2020 to 2040. The results show that energy intensity effect and energy structure effect can promote the optimization of the relationship between economy and carbon emissions in Henan Province. Energy structure and carbon emission intensity have a significant negative impact on carbon emissions, while industrial structure has a significant positive impact on carbon emissions. Henan Province can achieve the "carbon peak" goal by 2030 years under the standard and low-carbon development scenario, but it cannot achieve this goal under the high-speed development scenario. Therefore, in order to achieve the goals of "carbon peaking" and "carbon neutralization" as scheduled, Henan Province must adjust its industrial structure, optimize its energy consumption structure, improve energy efficiency, and reduce energy intensity.

[Multi-dimensional Analysis of the Synergistic Effect of Pollution Reduction and Carbon Reduction in Tianjin Based on the STIRPAT Model]

Based on the STIRPAT model, this study quantitatively analyzed the synergistic effect of pollution reduction and carbon reduction in Tianjin from three dimensions:total emission, emission reduction, and synergy coefficient. The results showed that the main emission sources of air pollutants and greenhouse gases in Tianjin were industrial sources, and the Pearson correlation coefficient of air pollutants and greenhouse gases was 0.984. The total population, urbanization rate, gross regional product, energy intensity, and carbon dioxide emission intensity were important factors affecting the synergistic effect of pollution reduction and carbon reduction in Tianjin. In 2011 and 2012, Tianjin's air pollutants and greenhouse gas emissions increased synergistically, and the synergistic effect coefficients were 0.18 and 0.17, respectively. From 2013 to 2014 and from 2018 to 2023, the air pollutant emission reduction and greenhouse gas emission increased, the synergistic effect coefficient was less than 0, and the pollution reduction and carbon reduction had no synergistic effect. In 2015-2017 and 2024-2060, air pollutants and greenhouse gas emissions were predicted to be reduced at the same time, with a synergistic effect coefficient ranging from 2.74 to 8.76. Tianjin had the conditions to enter the synergistic stage of pollution reduction and carbon reduction in 2024. The most important things for Tianjin to do to promote the synergy of pollution reduction and carbon reduction were to strictly control the total amount of greenhouse gas emissions, continue to promote the reduction in energy intensity and carbon dioxide emission intensity, and reasonably control the total population, urbanization rate, and regional GDP.

Study on the Impact of Collaborative Agglomeration of Manufacturing and Producer Services on PM2.5 Pollution: Evidence from Urban Agglomerations in the Middle Reaches of the Yangtze River in China

In this paper, using panel data of 28 cities in the middle reaches of the Yangtze River from 2003 to 2020 as the research sample, we built a dynamic spatial Durbin model based on the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model and conducted an empirical study on the impact of the coordinated agglomeration of manufacturing and producer services on particulate matter (PM) 2.5 pollution. The results show a significant positive spatial spillover effect of PM2.5 pollution in the middle reaches of the Yangtze River. The coordinated agglomeration of manufacturing and producer services in the urban agglomerations there is conducive to reducing PM2.5 pollution. Similar to the inverted-U curve of the classic environmental Kuznets curve hypothesis, there is a significant inverted-U curve relationship between PM2.5 pollution and economic growth in urban agglomerations in the middle reaches of the Yangtze River. The proportion of coal consumption, the proportion of secondary industry, and the urbanization level are significantly and positively correlated with PM2.5 pollution in urban agglomerations in this area. Technological innovation, environmental regulation, and annual average humidity play an important role in addressing the PM2.5 pollution and spatial spillover effect. Industrial structure and technological innovation are the main ways for the coordinated agglomeration of manufacturing and producer services to affect PM2.5. The research conclusion can be of great practical significance to optimize the regional industrial layout, control PM2.5 pollution, and establish a sustainable development policy system in the middle reaches of the Yangtze River in China.

An aging giant at the center of global warming: Population dynamics and its effect on CO2 emissions in China

Revealing the complex correlation between population aging and CO₂, and projecting their future dynamics are fundamentally necessary to inform effective policies toward a low-carbon and sustainable development in China. Differing from the existing studies, this study highlighted a quantitative investigation on the impact of aging on CO₂ emissions across the different stages of regional development in China through a STIRPAT model based on balanced provincial panel data from 1995 to 2019, and projected the demographic change and CO₂ emissions till 2050 by employing cohort model and scenario analysis. It is found that CO₂ emissions in China has witnessed a significant growth during 1995-2019, and will exhibit an inverted U-shaped growth till 2050 with its peak appears between 2030 and 2040. Statistically, every 1% growth of aging population will cause a 0.62% increase in CO₂ emissions in China. However, a big regional difference was also detected as aging contributed to CO₂ reduction in the eastern region, but stimulated CO₂ emissions in the central and western regions. Policy implications for achieving a low-carbon and aging-oriented sustainable development may include the integration of aging into the decision-making in industrial structure upgrading and CO₂ emission reduction at both national and region levels, the promotion of further transition to low-carbon consumption and green products in the eastern region, and strengthening the deep fusion of aging-oriented industries with local resource and environmental endowment in the central and western regions such as the development of eco-agriculture and green pension industries.

The varying roles of the dimensions of affluence in air pollution: a regional STIRPAT analysis for Germany

STIRPAT models investigate the impacts of population, affluence, and technology on the environment, with most STIRPAT studies revealing positive impacts of both population and affluence. Affluence is commonly defined as GDP per capita, but investigations of its impact largely neglect the possibility that increasing prosperity affects the environment in varying-even opposing-ways. This study addresses this gap by decomposing affluence into three dimensions-income per taxpayer, private car ownership, and the share of single-family houses-and analyzing their roles in the production of local NO_x emissions. Results for 367 German districts and autonomous cities between 1990 and 2020 indicate that, while private car ownership and single-family houses per capita can be considered drivers of local pollutants, such is not the case for income per taxpayer, which we find has a negative impact on NO_x emissions. The empirical findings suggest that policies should strengthen integrated mobility concepts and establish incentives that favor investment in modern heating or self-sufficiency systems.

The impact of economic uncertainty, economic growth and energy consumption on environmental degradation in MENA countries: Fresh insights from multiple thresholds NARDL approach

This paper explores the influence of economic policy uncertainty on environmental quality in selected MENA countries depending on an augmented STIRPAT model over the period 1970-2020. ARDL model and its extensions like augmented ARDL, augmented NARDL, and MTNARDL models are applied to detect any possible effect from uncertainty index to carbon dioxide (CO₂) emissions. The empirical results reveal the validity of environmental Kuznet curve (EKC) curve in all the countries. Moreover, the results show that the uncertainty index enhances environmental degradation, especially in extremely large changes in Morocco, Turkey, and Iran. Besides, the findings reveal that energy consumption and population in the entire sample escalates CO₂ emissions over the study period. Consequently, policymakers in MENA countries should consider the economic uncertainty index, particularly in light of its recent rise, when developing any strategies and plans aimed at improving environmental standards, as well as the need to encourage the use of renewable energies in order to increase the percentage of their contribution to total energy consumption.

Impact of Internet Development on Carbon Emissions in Jiangsu, China

Based on STIRPAT and panel threshold models, this study empirically tested the impact of Internet development on carbon emissions using panel data of Jiangsu Province from 2007 to 2020. The results showed that the carbon emissions intensity of the Internet development level had a significant promotion effect, while the carbon emissions intensity of technological progress showed a significant inhibition effect, but this inhibition effect is less than the promotion effect brought about by internet development. Considering the threshold effect, the development of the Internet had a double-threshold effect on carbon emissions in northern and central Jiangsu. Jiangsu Province should further accelerate the pace of Internet development and cross the threshold value as soon as possible. Finally, this study constructed a prediction model of emissions reduction to predict the future emissions reduction potential of Jiangsu Province and found that there was still much room for improvement regarding carbon emissions reduction in Jiangsu Province.

The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

Global warming and world-wide climate change caused by increasing carbon emissions have attracted a widespread public attention, while anthropogenic activities account for most of these problems generated in the social economy. In order to comprehensively measure the levels of carbon emissions and carbon sinks in Anhui Province, the study adopted some specific carbon accounting methods to analyze and explore datasets from the following suggested five carbon emission sources of energy consumption, food consumption, cultivated land, ruminants and waste, and three carbon sink sources of forest, grassland and crops to compile the carbon emission inventory in Anhui Province. Based on the compiled carbon emission inventory, carbon emissions and carbon sink capacity were calculated from 2000 to 2019 in Anhui Province, China. Combined with ridge regression and scenario analysis, the STIRPAT model was used to evaluate and predict the regional carbon emission from 2020 to 2040 to explore the provincial low-carbon development pathways, and carbon emissions of various industrial sectors were systematically compared and analyzed. Results showed that carbon emissions increased rapidly from 2000 to 2019 and regional energy consumption was the primary source of carbon emissions in Anhui Province. There were significant differences found in the increasing carbon emissions among various industries. The consumption proportion of coal in the provincial energy consumption continued to decline, while the consumption of oil and electricity proceeded to increase. Furthermore, there were significant differences among different urban and rural energy structures, and the carbon emissions from waste incineration were increasing. Additionally, there is an inverted "U"-shape curve of correlation between carbon emission and economic development in line with the environmental Kuznets curve, whereas it indicated a "positive U"-shaped curve of correlation between carbon emission and urbanization rate. The local government should strengthen environmental governance, actively promote industrial transformation, and increase the proportion of clean energy in the energy production and consumption structures in Anhui Province. These also suggested a great potential of emission reduction with carbon sink in Anhui Province.

The influence of urban form compactness on CO2 emissions and its threshold effect: Evidence from cities in China

Although compact urban form plays an important role in constraining emissions of carbon dioxide (CO₂), the boundary for the impact of compact urban form on these emissions has nevertheless received little attention. We consequently applied the entropy weight method and several key landscape metrics to a dataset from 295 cities in China to quantify urban form compactness (UFC) between 2000 and 2015. The STIRPAT model then was employed to estimate the impact of UFC on CO₂ emissions, and a panel threshold regression model was used to estimate threshold effects capable of limiting the impact of compact urban form on emissions. Although CO₂ emissions increased sharply over the 15-year study period, a significant negative relationship between UFC and CO₂ emissions was detected. Two thresholds of UFC were detected, and this allowed three categories to be differentiated: before the first threshold, between the two thresholds, and after the second threshold. These categories were respectively associated with no impact, strong impact, and weak impact of UFC on reduction of carbon emissions in the 295 cities. Carbon emissions reduction consequently becomes effective when the UFC exceeds the first threshold and effectiveness persists but at a reduced level when the UFC exceeds the second threshold. Further temporal analysis confirmed that an increasing number of mostly small- and medium-sized cities could constrain their future carbon emissions by adopting a compact urban form. Thus, government policies should emphasize UFC as a strategy to reduce CO₂ emissions. Moreover, by defining the range of compact urban form that has the greatest impact on CO₂ emissions, our study deepens the overall understanding of the influence of UFC on carbon emission reductions, so as to make contributions to the design of low-carbon cities.

The forecasting model research of rural energy transformation in Henan Province based on STIRPAT model

In order to find the model of rural energy transformation in Henan Province. In this paper, Tapio decoupling model is employed to investigate the pivotal factors affecting rural power consumption (PC) and total energy consumption (TEC) in Henan Province. In addition, PSO-BP is used to predict the values of each influencing factor in 2020-2025. Last, the STIRPAT model is used to forecast TEC and PC from 2020 to 2025 based on the data of rural energy consumption in Henan Province from 2009-2019. The results show that other factors besides population promote TEC and PC to different degrees. Moreover, the influencing factors, TEC and PC, form a virtuous cycle of mutual promotion. Then, TEC and PC consumption show an increasing trend year by year in 2020-2025. It is worth noting that after 2022, the variation of PC is greater than that of TEC. To sum up, improving rural electrification level is a necessary way to realize its low-carbon energy transition.

The "Pollution Halo" Effect of FDI: Evidence from the Chinese Sichuan-Chongqing Urban Agglomeration

In this paper, panel data from nineteen key cities in the Sichuan-Chongqing urban agglomeration from 2003 to 2016 were used as the study sample. Using the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, the effect of foreign direct investment (FDI) on particulate matter (PM_2.5) pollution and its action mechanism in the Sichuan-Chongqing urban agglomeration were considered for both socioeconomic and natural factors. The results showed that the "pollution halo" hypothesis of FDI in the Sichuan-Chongqing urban agglomeration has been supported. There are significant positive spatial spillover effects of PM_2.5 pollution in this urban agglomeration, and the introduction of FDI is conducive to alleviating PM_2.5 pollution in the urban agglomeration. Similar to the "inverted U" curve proposed by the environmental Kuznets curve (EKC) hypothesis, there was a significant "inverted U" curve relationship between PM_2.5 pollution and economic growth in the Sichuan-Chongqing urban agglomeration. However, there was a significant "U"-type curve relationship between the urbanization degree and the PM_2.5 concentration, which indicates that the current urbanization mode may aggravate the pollution degree of PM_2.5 in the urban agglomeration in the long term. Furthermore, the two natural factors of annual average temperature and annual precipitation play an important role in PM_2.5 pollution and spatial spillover effect in the Sichuan-Chongqing urban agglomeration. Economic development and rationalization of the industrial structure are the main ways by which FDI affects PM_2.5 pollution in the urban agglomeration. The research conclusions of this study can be of great practical significance to optimize the regional industrial layout, control PM_2.5 pollution, and establish a sustainable development policy system in the Sichuan-Chongqing urban agglomeration.

The effects of energy price, spatial spillover of CO2 emissions, and economic freedom on CO2 emissions in Europe: a spatial econometrics approach

Since the European Union (EU)'s current goal of making its continent the world's first climate-neutral continent by 2050, the EU will need to set a path for new policies in the areas of Europe's economy, energy consumption, and agriculture. Thus, this paper analyzes the marginal impact of energy price and economic freedom on Europe's CO₂ emissions grounded on the extended Stochastic Impacts by Regression on Population, Affluence and Technology ecology (STIRPAT) model together with the spatial econometric models. The results indicate the existence of spatial spillover effect of CO₂ emissions among some countries in Europe. The Hausman test was also performed to select the best model between the random effects and the fixed effects. The findings suggest that increasing both economic freedom and energy price in a local country turns to reduce the country's own CO₂ emissions and also reduces the emissions of its adjacent countries. Comparing the direct effect of economic freedom and energy price to that of the SDM fixed effect, a feedback of 12.77% and 23.53% of the direct effect was observed, respectively. The results also indicated that the turning point of economic freedom and economic growth was 6.714 and thus 9.083. Overall, the study spotlighted some policy suggestions for the energy market for the European commission in reducing the emissions of CO₂.

A scenario analysis of Chinese carbon neutral based on STIRPAT and system dynamics model

With the statement of Chinese government on energy saving in 2020 at the United Nations General Assembly, carbon neutral was widely spread as a new concept. As a big country, China has the responsibility and obligation to make its own contribution to global climate change. This paper aims to explore and find effective ways for China to achieve carbon neutrality by 2060. We identify the main factors affecting carbon emissions by STIRPAT model; combined with the scenario analysis, we divide the year 2020 to 2060 into three stages: year 2020-2030 is carbon peak stage, year 2030-2050 is rapid emission reduction stage, and year 2050-2060 is complete carbon neutralization stage. At each stage, three development models, high, medium, and low level, were established. There are a total of 27 different scenarios in three stages. A system dynamics model was established to simulate the effects of carbon emission factors and changes in carbon sinks in different scenarios. Finally, 8 paths were found which in line with Chinese current goal of achieving carbon neutrality with treating reach carbon peak in 2030 as an additional filter condition. Comparing per capita GDP levels in different scenarios, we eventually find that keeping economic development at a low level in the first stage, a high level in the second stage, and a medium level in the finally stage, the point where net carbon emissions are less than zero for the first time will appear between year 2056 and 2057. By then, the per capita GDP will reach 144,500 yuan (based on year 2000), nearly four times 2000's. In all, these findings are helpful for policymakers to implement reasonable policies to achieve carbon emission peaking and carbon neutral in China.

Key mitigation regions and strategies for CO2 emission reduction in China based on STIRPAT and ARIMA models

China is facing increasing pressure to reduce CO₂ emissions from energy consumption. Given this issue, understanding the characteristics, influencing factors, and trends can provide adequate information for decision-makers to solve the CO₂ emission problem. This study analyzes the characteristics of CO₂ emissions from energy consumption in 30 regions of China from 2005 to 2018 and applies the STIRPAT model to identify the impact of the influencing factors. Combined with the CO₂ emission trend in 2030 as predicted by the ARIMA model, the key mitigation regions and strategies reduction have been determined. Results indicate that CO₂ emissions have been increasing from 2005 to 2018 in China, thus showing the characteristic of the east being larger than the west spatially. Under the baseline scenario, these emissions will continue to rise in 2030. Carbon emissions intensity is declining, and the gap between provinces with the highest and lowest per capita CO₂ emissions is widening. Although per capita GDP is significantly positively correlated with provinces, population is the key factor influencing more provinces, followed by the proportion of the secondary industry and urbanization rate. To achieve low-carbon sustainable development, Shandong, Shanxi, Inner Mongolia, Guangdong, Shaanxi, Xinjiang, and Ningxia are considered the key regions of concern for emission reduction. The heterogeneity of CO₂ emission characteristics and influencing factors among regions provides a direction for the development of targeted and differentiated regional emission reduction strategies.

The effects of nurturing pressure and unemployment on carbon emissions: cross-country evidence

Nurturing pressure and unemployment affect our production and life in many ways. The aim of this study is to examine the potential effects of nurturing pressure and unemployment on global CO₂ emissions, by using the panel data of 77 countries and regions from 1991 to 2020 and a STIRPAT-based theoretical framework. The results show that at the global level, both nurturing pressure and unemployment overall have negative effects on CO₂ emissions. While at the regional level, it becomes a different situation. An increase in nurturing pressure leads to an increase in CO₂ emissions in the Americas and the Middle East and a decrease in CO₂ emissions in Africa, Europe, and Asia-Pacific. Unemployment has a positive effect on CO₂ emissions in the Middle East and a negative effect on CO₂ emissions in Africa, Americas, Europe, and the Asia-Pacific regions. There is no evidence that unemployment has certain effects on CO₂ emissions in the Middle East and the Asia-Pacific regions.

Exploring spatial heterogeneity and factors influencing construction and demolition waste in China

Natural disasters, new urbanization, and urban renewal activities generated a large amount of construction and demolition waste (C&DW), and managing C&DW has become an urgent problem to be solved in the construction of "Zero-waste cities." Based on the calculation of C&DW generation in China from 2005 to 2019, this study explored spatial heterogeneity and factors influencing C&DW in China by Exploratory Spatial Data Analysis (ESDA) and Geographically Weighted Regression (GWR) method. The results showed that C&DW generation in China increased every year, and the overall distribution was characterized as "high in the east and low in the west," with distinct regional differences. The generation intensity of C&DW in China showed a decreasing trend every year. The regions with rapid growth of C&DW generation were concentrated in the eastern coastal areas, and there was significant spatial heterogeneity in the growth trend. There is a significant spatial autocorrelation in C&DW generation in China. The factors of population size, per capita gross domestic product, and the scale of the construction industry played a positive role in promoting C&DW generation in each province, whereas labor efficiency played a negative role inhibiting C&DW generation, which has a significant temporal and spatial heterogeneity. The results extend C&DW management theory and help the policy maker to formulate regional differentiation policies as China and developing country.

Spatiotemporal pattern of regional carbon emissions and its influencing factors in the Yangtze River Delta urban agglomeration of China

Urbanization is a critical factor affecting regional carbon emissions. Clarifying the linkage between urbanization and carbon emissions can provide a decision-making reference to realize China's goal of carbon neutrality. This article examines the spatiotemporal patterns of urbanization and carbon emissions in the Yangtze River Delta urban agglomeration from 2008 to 2018. A complete set of variables is considered to construct relevant land and ecological urbanization variables, and the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and spatial Durbin model (SDM) are adopted to explore the impact of various driving factors on carbon emissions. The results indicate that (1) during the study period, the carbon emissions in the Yangtze River Delta urban agglomeration exhibited a fluctuating increase and that the incremental carbon emissions followed a downward trend. (2) Carbon emissions exhibited a positive spatial correlation. Cold- and hotspot areas indicated a three-gradient pattern from west to east, and a concentric circle radiation pattern occurred with Shanghai as the core. Carbon emissions were spatially imbalanced, but the centre of gravity slightly fluctuated, with a total migration distance of 38.48 km, indicating a migration trend towards the southeast. (3) Regarding the two considered dimensions of urbanization, all driving factors except urbanization played a role in carbon emission enhancement. Consequently, for every 1% increase in economic factors, the carbon emissions correspondingly increased by 0.43-0.57%. Hence, economic factors are the most important factors promoting increased carbon emissions. In the ecological urbanization dimension, urbanization caused a non-significant decrease in carbon emissions, while there was no spillover effect on carbon emissions in neighbouring areas. Accordingly, carbon emission reduction efforts should promote the transformation of urbanization from a land-driven process to an ecologically driven process and realize the synergies among carbon emission reductions, urban development, and land use.

Prediction and Trend Analysis of Regional Industrial Carbon Emission in China: A Study of Nanjing City

Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact factors of industrial carbon emission in Nanjing were considered as total population, industrial output value, labor productivity, industrialization rate, energy intensity, research and development (R&D) intensity, and energy structure. Among them, the total population, industrial output value, labor productivity, and industrial energy structure played a role in promoting the increase of industrial carbon emissions in Nanjing, and the degree of influence weakened in turn. For every 1% change in these four factors, carbon emissions increased by 0.52%, 0.49%, 0.17% and 0.12%, respectively. The industrialization rate, R&D intensity, and energy intensity inhibited the increase of industrial carbon emissions, and the inhibiting effect weakened in turn. Every 1% change in these three factors inhibited the increase of industrial carbon emissions in Nanjing by 0.03%, 0.07%, and 0.02%, respectively. Then, taking the relevant data of industrial carbon emissions in Nanjing from 2006 to 2020 as a sample, the gray rolling prediction model with one variable and one first-order equation (GRPM (1,1)) forecast and scenario analysis is used to predict the industrial carbon emission in Nanjing under the influence of the pandemic from 2021 to 2030, and the three development scenarios were established as three levels of high-carbon, benchmark and low-carbon, It was concluded that Nanjing’s industrial carbon emissions in 2030 would be 229.95 million tons under the high-carbon development scenario, 226.92 million tons under the benchmark development scenario, and 220.91 million tons under the low-carbon development scenario. It can not only provide data reference for controlling industrial carbon emissions in the future but also provide policy suggestions and development routes for urban planning decision-makers. Finally, it is hoped that this provides a reference for other cities with similar development as Nanjing.

Spatial spillover effects of urbanization on carbon emissions in the Yangtze River Delta urban agglomeration, China

To achieve a win-win situation for both urbanization and carbon emissions reduction from a spatiotemporal perspective, we need to identify the salient links between urbanization and carbon emissions in different dimensions. Using 2008-2018 panel data on the Yangtze River Delta urban agglomeration, this paper constructs a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model based on four dimensions of urbanization: population, economy, land, and ecology. Additionally, it uses a whole group of variables for reference, constructs a Spatial Durbin model (SDM) to estimate the spatial effect, and empirically investigates the spatial dependence of carbon emissions and the influence of various driving factors. The results show that (1) in the temporal dimension, the historical carbon emissions of the study area continue to increase. However, the extent to which they are doing so is slowing, the number of low carbon emissions areas has significantly decreased, the number of medium carbon emissions areas have significantly increased, the number of high and relatively high carbon emissions areas are relatively stable, and energy intensity continues to decline. (2) In the spatial dimension, Shanghai, Suzhou, and their surrounding cities have always been carbon emissions hotspots, high and relatively high carbon emissions areas are mainly concentrated in these cities. Low carbon emissions areas and cold spots are mainly distributed in Anhui Province. Medium carbon emissions areas show a great spatial and temporal evolution and are distributed in all provinces. (3) In the four dimensions of urbanization, per capita GDP will not only affect regional carbon emissions but also have a spatial spillover effect. For every 1% increase in the economic factors, carbon emissions in neighboring regions will increase by 0.38-0.43%. Population, economic, and technological factors have significant positive effects on carbon emissions, and economic factor is the most important factor. (4) In different dimensions of urbanization, there are obvious heterogeneities in the impacts of different factors on carbon emissions. Among them, the elasticity coefficient of per capita GDP and energy intensity is the smallest among the dimension of land urbanization, and the elasticity coefficient of the total population is the smallest among the dimension of population urbanization. Therefore, when formulating carbon emissions reduction policies, it is necessary to fully consider the spatial spillover effects, determine the optimal population size threshold, advocate for a low-carbon lifestyle, promote clean technology, and realize information exchange and policy interaction across regions from the perspective of holistic governance.

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

In the present study, the STIRPAT model was adopted to examine the impacts of several factors on dioxide emissions using the time series data from 2000 to 2019 in Xinjiang. The said factors included population aging, urbanization, household size, per capita GDP, number of vehicles, per capita mutton consumption, education level, and household direct energy consumption structure. Findings were made that the positive effects of urbanization, per capita GDP, per capita mutton consumption and education on carbon emissions were obvious; the number of vehicles had the biggest positive impact on carbon dioxide emissions; and household size and household direct energy consumption structure had a significantly negative impact on carbon emissions. Based on the aforementioned findings, the GA-BP neural network was introduced to predict the carbon emission trend of Xinjiang in 2020-2050. The results reveal that the peak time of the low-carbon scenario was the earliest, between 2029 and 2033. The peak time of the middle scenario was later than low-carbon scenario, between 2032 and 2037, while the peak time of the high-carbon scenario was the latest and was unlikely to reach the peak before 2050.

The Cause of China's Haze Pollution: City Level Evidence Based on the Extended STIRPAT Model

Based on the extended STIRPAT model, this paper examines social and economic factors regarding PM_2.5 concentration intensity in 255 Chinese cities from 2007 to 2016, and includes quantile regressions to analyze the different effects of these factors among cities of various sizes. The results indicate that: (1) during 2007–2016, urban PM_2.5 concentration exhibited declining trends in fluctuations concerning the development of the urban economy, accompanied by uncertainty under different city types; (2) population size has a significant effect on propelling PM_2.5 concentration; (3) the effect of structure reformation on PM_2.5 concentration is evident among cities with different populations and levels of economic development; and (4) foreign investment and scientific technology can significantly reduce PM_2.5 emission concentration in cities. Accordingly, local governments not only endeavor to further control population size, but should implement a recycling economy, and devise a viable urban industrial structure. The city governance policies for PM_2.5 concentration reduction require re-classification according to different population scales. Cities with large populations (i.e., over 10 million) should consider reducing their energy consumption. Medium population-sized cities (between 1 million and 10 million) should indeed implement effective population (density) control policies, while cities with small populations (less than 1 million) should focus on promoting sustainable urban development to stop environmental pollution from secondary industry sources.

Analysis of influencing factors of carbon emissions in resource-based cities in the Yellow River basin under carbon neutrality target

In 2020, China promised to achieve carbon peaking by 2030 and carbon neutrality by 2060, and these targets are famous as "Goal 3060" in China. Chinese resource-based cities are concerned about the realization of Goal 3060 to practice national action against environmental change. In this paper, this study evaluates the impact of population, economic growth, energy intensity, industrial structure, fixed asset investment, and urbanization level on carbon emissions in Chinese cities. To do so, the paper divides 36 Chinese cities into four types (growing city, mature city, recessionary city, and regenerative city) from 2003 to 2017 by factor investigation according to the diverse development stages. The extended STIRPAT model is used to assess the impact of various factors on CO₂ emissions in the Yellow River basin and diverse city levels. The panel regression analysis was conducted for the basin as a whole and cities at different development stages through a fixed-effects model and a linear regression model with Driscoll-Kraay standard errors. The results show that (1) the total carbon emissions in the Yellow River basin continued to climb during the study period. However, the growth rate slowed down significantly after 2012. In addition, there are differences in the total carbon emissions and growth rate of different cities. (2) Population, real GDP, energy intensity, industrial structure, and fixed asset investment all have a significant positive impact on carbon emissions in the overall basin except the urbanization level which has a significant negative influence on carbon emissions. (3) There is heterogeneity in the influencing factors of carbon emissions in resource-based cities at various development stages. Based on these results, corresponding policies are proposed for different types of cities to help resource-based cities achieve the 3060 dual carbon goal.

Scenario simulations for the peak of provincial household CO2 emissions in China based on the STIRPAT model

As household CO₂ emissions (HCEs) are a key source of China's CO₂ emissions, exploring the mitigation potential of HCEs is significant to achieve China's 2030 emission target. However, rare literatures analyzed the future evolution of HCEs from the provincial perspective. Here, we employ the STIRPAT model and build three scenarios (i.e., baseline, low and high scenarios) to investigate the trajectories and peak times of HCEs in 30 provinces up to 2040. The results show that 25 provinces can peak HCEs before 2030 in at least one scenario, while 5 provinces cannot achieve the 2030 emission target in any scenarios. Moreover, Guangxi and Hainan will maintain growth up to 2040 in all three scenarios. At the national level, China's household sector can achieve HCEs peak in all three scenarios. Further reduction of emission intensity helps national HCEs reach the peak around 2025 in the high scenario at 1063 MtCO₂. The findings suggest that Guangdong, Jiangsu, Hebei, Henan, Zhejiang and Anhui are key provinces for future HCEs reductions, because they account for more than 40% of national HCEs in 2040 in all three scenarios. Energy efficiency improvement and clean energy applications will be effective for emission reductions.

What drives the export-related carbon intensity changes in China? Empirical analyses from temporal-spatial-industrial perspectives

This study aims to explore the driving determinants on the export-related carbon intensity (ECI) of China, to better understand the impact of international trade on climate change governance and facilitate China's carbon intensity mitigation goals. First, China's ECI evolution and its gaps with the USA and India are measured during 2002-2014. Then, the main drivers of China's ECIvert study further discusses the influencing factors of ECI in the manufacturing industry using the environmental-extended STIRPAT model and GMM method. The results show that (1) China's overall ECI increases from 1.50 Kg/US$ in 2002 to 1.92 Kg/US$ in 2005 and then decreases to 1.27 Kg/US$ in 2014. The ECI of the manufacturing industry is significantly higher than that of the agriculture and service industry. China's ECI gap with the USA is greater than that with India, and both show a downward trend. (2) Carbon emission coefficient is the domain factor to reduce China's ECI during 2002-2014; the effects of the value-added coefficient, input-output structure, and final demand are limited. The input structure dominantly expands China's ECI gaps both with the USA and India, followed by the value-added coefficient. The carbon emission coefficient enlarges the ECI gap with the USA while reduces that with India. (3) Industrial productivity and value-added rate are negatively correlated with ECI in the manufacturing industry, while per capita capital stock plays the opposite role. The positive correlation between energy intensity and CIE becomes significant after distinguishing technology heterogeneity. In contrast to the non-tech-intensive manufacturing industry, the increase of backward GVCs participation of tech-intensive ones will reduce the ECI. The threshold effect of backward GVCs participation exists in the whole manufacturing industry. Targeted ECI reduction policy implications are suggested.

A study on evaluation and influencing factors of carbon emission performance in China's new energy vehicle enterprises

Vehicle industry has made great contribution to human progress. However, in the process of vehicle operation, a large number of carbon compounds are emitted, which brings serious environmental problems. As one of the important means of vehicle carbon emission governance, the development of new energy vehicles (NEVs) has attracted much attention. The behavior and performance of NEV enterprises are highly concerned. Using Chinese 23 NEV vehicle enterprises' data from 2011 to 2018, this paper evaluates the carbon emission performance with the super-efficiency slacks-based measure (SE-SBM) model based on undesirable output and then constructs STIRPAT model to analyze the influencing factors of carbon emission performance. The results indicate that, firstly, the carbon emission performance of China's NEV enterprises is increasing year by year. Secondly, the carbon emission performance of different NEV enterprise is distinct in the same year, and the carbon emission performance of the same NEV enterprise is distinct in different year. Thirdly, technological innovation, government support, and free cash flow have significant positive impact on the carbon emission performance of NEV enterprises, while debt constraint, energy intensity, and enterprise size have a significant negative impact on the carbon emission performance of NEV enterprises.

Impact of Income, Density, and Population Size on PM2.5 Pollutions: A Scaling Analysis of 254 Large Cities in Six Developed Countries

Despite numerous studies on multiple socio-economic factors influencing urban PM2.5 pollution in China, only a few comparable studies have focused on developed countries. We analyzed the impact of three major socio-economic factors (i.e., income per capita, population density, and population size of a city) on PM2.5 concentrations for 254 cities from six developed countries. We used the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model with three separate data sets covering the period of 2001 to 2013. Each data set of 254 cities were further categorized into five subgroups of cities ranked by variable levels of income, density, and population. The results from the multivariate panel regression revealed a wide variation of coefficients. The most consistent results came from the six income coefficients, all of which met the statistical test of significance. All income coefficients except one carried negative signs, supporting the applicability of the environmental Kuznet curve. In contrast, the five density coefficients produced statistically significant positive signs, supporting the results from previous studies. However, we discovered an interesting U-shaped distribution of density coefficients across the six subgroups of cities, which may be unique to developed countries with urban pollution. The results from the population coefficients were not conclusive, which is similar to the results of previous studies. Implications from the results of this study for urban and national policy makers are discussed.

Chinese household environmental footprint and its response to environmental awareness

Sustainable consumption has become an important issue when the world has limited resources and deteriorating environment. Given the context, this study first examines the influence of income disparities on different environmental footprints of Chinese households. We combine the input-output model with Chinese household survey data from the China family panel studies. The results show that, on an average, the carbon dioxide (CO₂), pollutants, water and energy footprints of richest families are about 4.5-5.9 times larger than that of poorest households. Furthermore, the richest families even have 9.2-11.5 times larger metal and non-metal footprints. The consumption structure change can act as a driving factor to offset the increase in CO₂, pollutants, water and energy footprints brought about by income rise, since it has reduced the household footprints per unit expenditure. However, the consumption structure change may increase the metal and non-metal footprints per unit expenditure simultaneously, making the metal and non-metal footprints increase faster than the other footprints as income increases. Since environment awareness is expected as a factor to further restrain household environment footprints on the demand side, we also examine how one important component of environmental awareness-perceived seriousness of environmental problems-influences household footprints based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. While emphasizing seriousness of environmental issues can cause a slight decline in the metal and non-metal footprints, it surprisingly increases the CO₂, energy, and pollutants footprints; it does not influence the water footprint. In addition, perception of the seriousness of environmental problems impacts the environmental behaviors of wealthy families more than poor families. These findings demonstrate the need to formulate policies to overcome the demand-side challenge of achieving sustainability.

Gauging the impacts of urbanization on CO2 emissions from the construction industry: Evidence from China

The construction industry has aided rapid urbanization in China, significantly contributing to CO₂ emissions. However, few studies have investigated the impacts of urbanization on CO₂ emissions from the construction industry and the regional heterogeneity or considered the construction-related factors for urban construction scale to represent urbanization. To compensate for these limitations, this study aimed to explore the impacts of urbanization on CO₂ emissions from the construction industry. Herein, the urban construction scale was used to represent urbanization, along with population size, economic growth, and technology level. An augmented Stochastic Impacts by Regression on Population, Affluence, and Technology model was used to estimate the cross-province panel data from three regions in China during 2008-2017. The heterogeneity due to regional differences in urbanization levels was addressed by classifying China into three regions- urbanized, urbanizing, and under-urbanized. The findings suggest that population size, economic growth, construction of residential buildings, and technology level were the primary factors impacting CO₂ emissions, and the impact presented a declining trend from the urbanized to the urbanizing and under-urbanized regions. Specifically, an inverted U-shaped relationship existed between CO₂ emissions and urban economic growth, and the urbanized region indicated a higher inflection point than other regions. The urbanization ratio was negatively correlated with CO₂ emissions, while the energy intensity, per capita floor space of urban residential buildings, and per capita length of drainpipes were positively correlated with the CO₂ emissions in all three regions. Further, the technology level was conducive to CO₂ emissions reduction, however, it requires further improvement. The per capita area of paved roads exerted significantly negative effects in the urbanized region and insignificant in the urbanizing and under-urbanized regions. Overall, these results can help formulate policies to mitigate the construction industry's carbon emissions.

Nonlinear influence of urbanization on China's urban residential building carbon emissions: New evidence from panel threshold model

Carbon mitigation in the urban residential building sector is critical for China to achieve its carbon peak and carbon neutral commitment. However, how urbanization affects urban residential building carbon emissions is still unclear. This study adopts the panel threshold regression model to explore the dynamic influence mechanism of the urbanization on urban residential building CO₂ emissions based on the evidence from China's 30 provincial regions during 2000-2015. Results indicate that urbanization contributes positively to the increase of urban residential building CO₂ emissions, while such degree of influence varies across different stages of income and energy structure. As for per capita income, the promoting effect of the urbanization on urban residential building CO₂ emissions is enhanced with the growth of per capita income. And the degree of such increasing effect becomes greater when per capita income exceeds its threshold value. Regarding the energy mix, the driving effect of urbanization on urban residential building CO₂ emissions is also strengthened when the energy mix crosses its threshold value, showing a "stepwise growth" feature. This study reveals the nonlinear influence mechanism of urbanization on urban building CO₂ emissions, and this is helpful in boosting the related theoretical and practical exploration on the impact of urbanization on the environment. Based on our findings, an environmentally-friendly consumption pattern should be promoted and more penetration of cleaner energies should be improved in urban households, which will be effective to alleviate the increase of residential carbon emissions.