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Background: As a key agricultural region in China, Heilongjiang Province has experienced significant carbon emissions over the past few decades. To understand the underlying factors and future trends in these emissions, a comprehensive analysis was conducted from 1993 to 2030. Methods: The agricultural carbon emissions from 1993 to 2020 were estimated using the emission factor method. To analyze the influencing factors and future trends of these emissions, the study employed the Logarithmic Mean Divisia Index (LMDI) and integrated it with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. Results: Results showed that (1) the agricultural carbon emissions in Heilongjiang were primarily driven by rice cultivation, followed by fertilizer production and irrigation electricity. (2) The economic and labor structure effects were the main driving factors of agricultural carbon emissions, while the population, demographic, and intensity effects were the main inhibitors. (3) Agricultural carbon emissions in Heilongjiang Province peaked in 2016 with 69.6 Mt CO2-eq and could subsequently decline by -3.92% to -4.52% between 2020 and 2030 in different scenario simulations. In the future, Heilongjiang Province should prioritize the reduction of agricultural carbon emissions from rice production. Adjusting the planting structure, managing the layout of rice paddies, and promoting the cultivation of dry rice varieties would significantly contribute to mitigating agricultural carbon emissions.

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Promoting the green development of the logistics industry has become a focal point of attention in China. This study combines an improved gravity model and social network analysis method, focusing on the nineteen provinces of the Yangtze Economic Belt and Yellow River Basin. It constructs a spatial correlation network of carbon emissions in the logistics industry from 2010 to 2021, exploring its formation mechanism and spatial evolution characteristics. The study utilizes a quadratic assignment procedure model to investigate internal driving factors. Building upon this, the study employs an improved STIRPAT model to predict the emission reduction path of the future logistics industry. Results are as follows: (1) Carbon consumption is most pronounced in Shandong, Jiangsu, and Shanghai. The carbon emission shows the characteristics of larger downstream; (2) The bridges of carbon emission-related networks gradually shift from Shandong to Shanxi, Anhui, and Sichuan. Carbon emissions in each sector and spatial spillover effects exhibit dynamic correlations and interactive influences; (3) Energy intensity, freight volume, and the spatial correlation network of the logistics industry are highly correlated; (4) The overall carbon emissions from the logistics industry show a decreasing trend in the future. Anhui and Shaanxi provinces will have high carbon emissions in 2035. The conclusions aim to provide policy suggestions for the region's low-carbon transformation.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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The "double carbon" goal has proposed new "green" requirements for China's low-carbon economic development, and green technology innovation (GTI) has become an important way to coordinate economic and sustainable development. The study explores the spatial-temporal evolution of carbon emission intensity (CEI) of Chinese prefecture-level cities, analyses the nonlinear impact of GTI on the CEI by constructing a panel quantile model, and draws the following conclusions. First, CEI shows a decreasing trend from 2006 to 2019 and a spatial distribution pattern of "high in the north and low in the south, high in the west and low in the east". Second, GTI significantly reduces CEI, and as the quantile point increases, the carbon reduction effect of GTI is characterized by a U-shaped change, decreasing first and then increasing. Overall, GTI has a significantly more profound inhibiting effect on high CEI regions than on low CEI regions. Third, there is spatial heterogeneity in the impact of GTI on CEI across the four major regions and diverse policy contexts. The study proposes countermeasures for low-carbon development in terms of tapping the potential of GTI, strengthening its regional synergy, and applying locally appropriate measures, to gain the great practical significance for achieving the double carbon target.

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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.

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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.

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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.

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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.

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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.

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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.