Search Results (61)

In order to achieve the carbon peaking and carbon neutrality goals in China’s transportation sector, this paper examines the regional data in transportation carbon emissions across China, investigates the shifting trends of the carbon emission centroid over time, and proposes a novel representation using fuzzy set theory and rough set theory for carbon emission prediction. This paper employs the ESDA model to analyze the regional distribution of carbon emissions in the transportation sector across 30 provinces in China for the years 2005, 2010, 2015, and 2020. Utilizing the economic centroid model and standard deviation ellipse, the trend of carbon emission centroid shifts in China’s transportation sector is examined, revealing that the carbon emission centroid for all four time points is located in Henan Province. Subsequently, focusing on Henan Province, ridge regression analysis is conducted to identify the driving factors influencing carbon emissions in the transportation sector from 2005 to 2020. Lastly, a combined approach integrating scenario analysis and the STIRPAT model is employed to forecast carbon emissions in the transportation sector of Henan Province for the period 2021–2035. The findings suggest that high-carbon-emission regions in China’s transportation sector gradually extend from the eastern coastal areas to the southwestern regions, with an overall trend of the carbon emission centroid shifting northward. The carbon emission centroid for the years 2005, 2010, 2015, and 2020 is consistently located in Henan Province. Ridge regression analysis indicates that population size, transportation energy consumption intensity, energy structure, transportation economic share, and per capita GDP all have promoting effects on carbon emissions in Henan Province’s transportation sector. Based on the combined approach of scenario analysis and the STIRPAT model, it is predicted that the transportation sector in Henan Province may reach its carbon peak between 2027 and 2029. These conclusions facilitate the formulation of region-specific emission reduction policies and measures tailored to the transportation sector. Full article

South Africa’s transport sector remains a major contributor to greenhouse gas emissions, yet limited empirical evidence exists on the environmental drivers of public transport emissions at the provincial level. This study applies an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework within a dynamic panel setting to examine the determinants of provincial public transport emissions across nine South African provinces from 2015 to 2022. Rather than conducting economy-wide emissions accounting, the analysis focuses on transport-specific drivers relevant to public passenger mobility, including population, income, fuel consumption, infrastructure investment, and modal usage. A Bias-Corrected Method of Moments (BCMM) estimator is employed to address emission persistence, endogeneity, and small-sample bias, with pooled ordinary least squares and fixed-effects models used for robustness. Province fixed effects are used to control for unobserved regional heterogeneity, while common dynamic elasticities are estimated for key influencing factors. The results reveal strong dependence on emissions, indicating substantial structural persistence over time. GDP per capita emerges as the dominant and statistically significant driver of public transport emissions, while population, urbanisation, fuel consumption, transport infrastructure investment, and modal usage (road and rail) are statistically insignificant once dynamics and unobserved heterogeneity are controlled. These findings suggest that public transport emissions in South Africa are driven primarily by economic growth and entrenched structural factors rather than short-run changes in transport systems. Policy implications highlight the need for sustained low-carbon investment, technological transition, and integrated transport planning to decouple economic growth from emissions and support progress toward Sustainable Development Goals 11 and 13. Full article

The accounting and analysis of agricultural grey water footprint (AGWF) are crucial for building a low-water-consumption agricultural production model and improving water resource efficiency in Fujian Province. This study innovatively integrated green development indicators into an extended STIRPAT model, quantitatively analyzed the drivers of AGWF from six dimensions (population, economy, technology, dietary structure, meteorology, and green development) based on data from 2009 to 2023. The results indicated that the AGWF in Fujian Province exhibited an overall upward trend, increasing from 114.61 billion m³ to 221.30 billion m³. Population expansion (elasticity: 0.49853) and economic growth (elasticity: 0.46329) were identified as the primary positive drivers, while technological progress exerted a mitigating effect (elasticity: −0.07253). The impacts of dietary structure, precipitation, and green development measures, though statistically significant, were quantitatively limited within the study period (elasticities of 0.0312, 0.0273, and 0.004, respectively). These findings provide quantitative support for formulating targeted policies for agricultural water resource management and non-point source pollution control in regions with similar characteristics. Full article

The rapid development of industry has led to intensive energy and resource consumption, increasing carbon emissions. As key areas for carbon control, metropolitan regions play an essential role in China’s urbanization and regional development, yet research on predicting industrial carbon emissions remains insufficient. This study takes the Hangzhou Metropolitan Area in China as a case study and employs an extended STIRPAT model to predict industrial carbon emissions from 2024 to 2050 across different scenarios. The results show that industrial carbon emission intensity has the most significant impact on carbon emissions, followed by urbanization, population, economy, industrial structure, technology, energy intensity, and openness. The peak time of industrial carbon emissions varies significantly under different scenarios. The peak appears in 2026 under the deep emission reduction scenario, in 2028 under the green economy scenario, in 2030 under the baseline scenario, and does not occur by 2050 under the extensive development scenario. The green economy scenario achieves effective emission reductions with the least economic impact and is superior to the single-emission-reduction-oriented deep-emission-reduction scenario. This study responds to China’s “dual-carbon” strategy and provides a replicable and transferable regional pathway for industrial decarbonization and policy-making in other metropolitan areas. Full article

The rapid growth of Waste Electrical and Electronic Equipment (WEEE) in the European Union highlights the need for a rigorous understanding of its long-term dynamics and the role of innovation in shaping its trajectory. This study investigates how innovation influences the dynamics of WEEE generation in the European Union. We develop an innovation-adjusted mathematical model of e-waste as a stock flow system and prove the existence and global stability of a unique positive equilibrium. The model analytically generates an environmental Kuznets-type turning point and shows that innovation reduces waste accumulation by accelerating effective depreciation. To link the theoretical results with empirical patterns, we embed the model in a STIRPAT panel specification using annual data for 27 EU member states from 2013 to 2023, where EU Eco-innovation Index (EEI) serves as a composite index which directly captures policy-driven green technology and circular economy activities, aligning precisely with our theoretical framework. We also extend the quasi-demeaning transformation to panels with correlated shocks and establish its consistency under a factor structured error process. The empirical estimates confirm a positive effect of income on WEEE at lower development levels and a negative coefficient on its squared term, consistent with an inverted U pattern, while innovation is associated with lower waste intensity. These findings demonstrate how mathematical modeling can strengthen the interpretation of macro panel evidence on circularity and provide a basis for future optimization of innovation driven sustainability transitions. Full article

Against the backdrop of China’s “dual carbon” strategy (carbon peaking and carbon neutrality), provincial-level carbon emission research is crucial for the implementation of related policies. However, existing studies insufficiently cover the driving mechanisms and scenario prediction for energy-importing provinces. This study can provide theoretical references for similar provinces in China to conduct research on carbon dioxide emissions from energy consumption. The carbon dioxide emissions from energy consumption in Jiangsu Province between 2000 and 2023 were calculated using the carbon emission coefficient method. The Tapio decoupling index model was adopted to evaluate the decoupling relationship between economic growth and carbon dioxide emissions from energy consumption in Jiangsu. An extended STIRPAT model was established to predict carbon dioxide emissions from energy consumption in Jiangsu, and this model was applied to analyze the emissions under three scenarios (baseline scenario, low-carbon scenario, and enhanced low-carbon scenario) during 2024–2030. The results show the following: (1) During 2000–2023, the carbon dioxide emissions from energy consumption in Jiangsu Province ranged from 215.22428 million tons to 783.94270 million tons, with an average of 549.96280 million tons. (2) The decoupling status between carbon dioxide emissions from energy consumption and economic development in Jiangsu was dominated by weak decoupling, accounting for 91.304%, while a small proportion (8.696%) of expansive coupling was also observed. (3) Under the baseline scenario, the carbon dioxide emissions from energy consumption in Jiangsu in 2030 will reach 796.828 million tons; under the low-carbon scenario, the emissions will be 786.355 million tons; and under the enhanced low-carbon scenario, the emissions will be 772.293 million tons. Furthermore, countermeasures and suggestions for reducing carbon dioxide emissions from energy consumption in Jiangsu are proposed, mainly including strengthening the guidance of policies and institutional systems, optimizing the energy consumption structure, intensifying technological innovation efforts, and enhancing government promotion and publicity. Full article

This study intends to portray how varying degrees of environmental policy stringency and the growing pressure of global competition reflect on high-tech (HT) sectors’ cost rationalization strategies and lead to environmental consequences in 15 G20 countries (1992–2019). Moreover, we center the pattern of cost rationalization management regarding the opportunity cost of ecosystem service consumption and propose to test the fundamental hypothesis stating the possible transmission of competition-induced technological innovations to green economic transformation. Our new methodology estimates quantile-specific effects with MM-QR, while identifying the main interaction effects between regulatory pressure and trade competition uses an extended STIRPAT model. The results reveal a paradoxical finding: despite higher environmental policy stringency and opportunity costs of ecosystem services, HT sectors persistently adopt environmentally detrimental cost-reduction approaches. These findings carry important policy implications: (1) environmental regulations for HT sectors require complementary innovation subsidies, (2) trade agreements should incorporate clean technology transfer clauses, and (3) governments must monitor sectoral emission leakage risks. Our dual machine learning–econometric approach provides policymakers with targeted insights for different emission scenarios, highlighting the need for differentiated strategies across clean and polluting HT subsectors. Full article

As China commits to reaching peak carbon emissions and achieving carbon neutrality, accurately predicting the provincial carbon peak year is vital for designing effective, region-specific policies. This study proposes an integrated approach based on extended STIRPAT and GA-BiLSTM models to predict China’s provincial carbon peak year. First, based on panel data across 30 provinces in China from 2000 to 2023, we construct a multidimensional indicator system that encompasses socioeconomic factors, energy consumption dynamics, and technological innovation using the extended STIRPAT model, which explains 87.42% of the variation in carbon emissions. Second, to improve prediction accuracy, a hybrid model combining GA-optimized BiLSTM networks is proposed, capturing temporal dynamics and optimizing parameters to address issues like overfitting. The GA-BiLSTM model achieves an R² of 0.9415, significantly outperforming benchmark models with lower error metrics. Third, based on the model constructed above, the peak years are projected for baseline, low-carbon, and high-carbon scenarios. In the low-carbon scenario, 19 provinces are projected to peak before 2030, which is 8 more than in the baseline scenario. Meanwhile, under the high-carbon scenario, some provinces such as Jiangsu and Hebei may fail to peak by 2040. Finally, based on the predicted carbon peak year, provinces are categorized into four pathways—early, recent, later, and non-peaking—to provide targeted policy recommendations. This integrated framework significantly enhances prediction precision and captures regional disparities, enabling tailored decarbonization strategies that support China’s dual carbon goals of balancing economic growth with environmental protection. The approach provides critical insights for region-specific low-carbon transitions and advances sustainable climate policy modeling. Full article

Global resource depletion has intensified scrutiny on Sustainable Development Goal 12 (SDG12), where consumption-based material footprint serves as a critical sustainability metric. Despite its transformative potential, the paradoxical role of Information and Communication Technology (ICT) in resource conservation remains underexplored. This study adopts an extended STIRPAT model as the analytical framework. It employs the Method of Moments Quantile Regression to evaluate the non-linear effects of digitalization-related indicators and other influencing factors on material footprint. The analysis is conducted across different quantiles for G20 countries from 2000 to 2020. The results show that (1) ICT exhibits a substantial positive effect on consumption-based material footprint under all quantiles. This leads to an increase in the material footprint, hindering the G20’s progress toward achieving SDG12. (2) The impact of ICT varies notably, with a more pronounced adverse effect on SDG12 in countries with higher resource consumption. (3) ICT goods export trade, technological innovation, and globalization significantly mitigate ICT’s adverse impact on resource consumption. This study provides targeted recommendations for G20 countries on how to leverage ICT to achieve SDG12 more effectively. Full article

In recent years, the digital economy (DE) has gained significant attention for its potential in reducing carbon emissions (CE). This paper intends to explore the regional carbon reduction effect of the DE and the entrepreneurship of small and medium-sized manufacturing enterprises (SMMEs), as well as disclose the mechanism through which the entrepreneurship of SMMEs functions. To this end, this paper employs an extended STIRPAT model to analyze the panel data of 30 provinces in China spanning from 2011 to 2018. The empirical analysis shows that (1) the DE has a positive effect on reducing regional total carbon emissions (TCE) and carbon emissions intensity (CEI); (2) the entrepreneurship of SMMEs has a negative influence on reducing regional CE; (3) the entrepreneurship of SMMEs fully mediates the link between the DE and TCE and partially mediates the relationship between the DE and the CEI; and (4) the DE has a stronger carbon reduction effect in regions with low urbanization levels and low institutional quality, as well as non-industrial pilot areas. The findings provide empirical evidence to policymakers on promoting CE reduction and the DE. This study has practical value for SMMEs to improve competitiveness and survival under the current environment. Full article

Residential carbon emissions (RCEs) have become a major contributor to China’s overall carbon emission growth. A comprehensive analysis of the evolution characteristics of regional disparities in RCEs at the urban level, along with a thorough examination of the driving factors behind RCEs and the convergence, is crucial for achieving carbon reduction goals within regions. This study calculates the RCEs of 270 cities in China from 2011 to 2019 based on multiregional input–output tables and explores the regional differences and spatiotemporal evolution characteristics of RCEs using the Dagum Gini coefficient decomposition method and kernel density estimation. On this basis, we examine the driving factors of RCEs using an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) econometric model and further analyze the convergence of RCEs by introducing a β-convergence model. The results are as follows: (1) The regional disparity of RCEs in China generally shows a wave-like declining trend, with the primary source of this disparity being the differences between city tiers. (2) Kernel density estimation shows that the greater the urban rank, the larger the regional disparity; the RCE distribution in third- and lower-tier cities is more concentrated. (3) Population density, population aging, and education level significantly exert a negative influence on RCEs, whereas economic development level, number of researchers, and number of private cars are positively correlated with RCEs. (4) Each urban agglomeration’s RCEs exhibits significant β-convergence, but the driving factors of RCEs and their convergence differ significantly across the urban agglomerations. This study provides targeted policy recommendations for China to achieve its emission reduction goals effectively. Each city cluster should tailor its approach to strengthen regional collaborative governance, optimize urban layouts, and promote low-carbon lifestyles in order to facilitate the convergence of RCEs and low-carbon transformation. Full article

Urban agglomerations with a high economic activity and population density are key areas for carbon emissions and pioneers in achieving carbon peaking and the Sustainable Development Goals (SDGs). This study combines machine learning with an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to uncover the mechanisms driving carbon peaking disparities within these regions. It forecasts carbon emissions under different scenarios and develops indices to assess peaking pressure, reduction potential, and driving forces. The findings show significant carbon emission disparities among cities in the Yangtze River Delta, with a fluctuating downward trend over time. Technological advancement, population size, affluence, and urbanization positively impact emissions, while the effects of industrial structure and foreign investment are weakening. Industrially optimized cities lead in peaking, while others—such as late-peaking and economically radiating cities—achieve peaking only under the ER scenario. Cities facing population loss and demonstration cities fail to peak by 2030 in any scenario. The study recommends differentiated carbon peaking pathways for cities, emphasizing tailored targets, pathway models, and improved supervision. This research offers theoretical and practical insights for global urban agglomerations aiming to achieve early carbon peaking. Full article

With the increasing energy use and carbon emissions in the transportation industry, its impact on the greenhouse effect is gradually being recognized. Therefore, this study aims to explore the achievement of carbon emission peak and carbon neutrality in transportation through prediction. The research employs a deep learning model, the CNN-GRU-Attention model, to predict carbon emissions in the transportation industry in Jiangsu, China. We select influencing factors through an extended STIRPAT model coupled with Lasso regression, and construct the CNN-GRU-Attention traffic carbon emission prediction model according to data indicators from 1995 to 2021. The model predicts carbon emissions from the transportation industry in Jiangsu Province between 2022 and 2035 under six distinct scenarios and proposes corresponding emission reduction strategies. The results show that the model in this study has higher prediction accuracy compared with other models, with a mean absolute error (MAE) of 0.061582, root mean square error (RMSE) of 0.085025, and R² of 0.91609 on the test set. Scenario-based predictions reveal that emission peak in the transportation industry in Jiangsu Province can be achieved under the clean development and comprehensive low-carbon scenarios, with technological innovation being the primary driver of low-carbon emission reductions. This study provides a novel approach for forecasting carbon emissions from the transportation industry and explores the implementation path of emission peak through this method. Full article

Analyzing the carbon emission behavior of a regional integrated energy system (RIES) is crucial for aligning with carbon-peaking development strategies and ensuring compliance with carbon-peaking implementation pathways. This study focuses on a building cluster area in Shanghai, China, aiming to provide a comprehensive analysis from both macro and micro perspectives. From a macro viewpoint, an extended STIRPAT model, incorporating the environmental Kuznets curve, is proposed to predict the carbon-peaking trajectory in Shanghai. This approach yields carbon-peaking implementation pathways for three scenarios: rapid development, stable development, and green development, spanning the period of 2020–2040. At a micro scale, three distinct RIES system configurations—fossil, hybrid, and clean—are formulated based on the renewable energy penetration level. Utilizing a multi-objective optimization model, this study explores the carbon emission behavior of a RIES while adhering to carbon-peaking constraints. Four scenarios of carbon emission reduction policies are implemented, leveraging green certificates and carbon-trading mechanisms. Performance indicators, including carbon emissions, carbon intensity, and marginal emission reduction cost, are employed to scrutinize the carbon emission behavior of the cross-regional integrated energy system within the confines of carbon peaking. Full article

Urbanization affects economic production activities and energy demand, as well as lifestyle and consumption behavior, affecting carbon dioxide emissions. This study constructs the System Generalized Method of Moments (Sys-GMM) model of the impact of urbanization rate on carbon dioxide emissions based on panel data of 136 countries and regions in the world from 1990 to 2020, grounded on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. This study found that (1) there is a negative relationship between urbanization rate and CO₂ emissions from 1990 to 2020. (2) The impact of the urbanization rate on CO₂ emissions is heterogeneous. An increase in urbanization rate in non-OECD countries significantly reduces CO₂ emissions, while the effect is not significant in OECD countries. (3) The carbon intensity of fossil energy consumption moderates the relationship between urbanization rate and CO₂ emissions, weakening the effect of urbanization rate on CO₂ emissions. Based on these findings, policy recommendations such as promoting urbanization and increasing the regulation and control of fossil energy carbon intensity are proposed. Full article