Search Results (269)

The carbon dioxide spillover effects and influencing factors of the “Belt and Road Initiative” (BRI) in African countries must be assessed to evaluate the effectiveness, promote low-carbon transmissions in African countries, and provide recommendations for achieving the 2030 Sustainable Development Goals. This novel study employs carbon dioxide emission intensity (CEI) and per capita carbon dioxide emissions (PCE) as dual indicators to evaluate the spatial spillover effects of 54 BRI African countries on their neighboring countries’ carbon emissions from 2007 to 2023. It identifies the key factors and mechanisms affecting these spillover effects using the spatial differences-in-differences (SDID) model. Results indicate that since the launch of the BRI, the CEI and PCE of BRI African countries have significantly increased, largely due to trade patterns and industrialization structures. Greater trade openness has further boosted local economic development, thereby increasing carbon dioxide’s spatial spillover. Government management and corruption control levels show some heterogeneity in the spillover effects, which may be attributed to long-standing issues of weak institutional enforcement in Africa. Overall, this study reveals the complex relationship between BRI African economic development and environmental outcomes, highlighting the importance of developing sustainable development strategies and establishing strong differentiated regulatory regimes to effectively address environmental challenges. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

As the climate problem is getting more and more serious and the “low-carbon revolution” of globalization is emerging, the logistics industry, as a high-end service industry, must also take the road of low-carbon development. Improving logistics carbon emission efficiency (LCEE) is gradually becoming an inevitable choice to maintain sustainable social development. The study uses the Super-SBM (Super-Slack-Based Measure) model to evaluate the urban LCEE from 2013 to 2022, explores the contribution of efficiency changes and technological progress to LCEE through the decomposition of the GML (Global Malmquist–Luenberger) index, and reveals the influence of digital transformation and energy consumption structure on LCEE by using the Spatial Durbin Model, concluding as follows: (1) LCEE declines from east to west, with large regional differences. (2) LCEE has steadily increased over the past decade, with slower growth from east to west. It fell in 2020 due to COVID-19 but has since recovered. (3) LCEE shows a catching-up effect among the three major regions, with technological progress being a key driver of improvement. (4) LCEE has significant spatial dependence. Energy consumption structure has a short-term negative spillover effect, while digital transformation has a positive spillover effect. Full article

The accelerating growth of the digital economy (DE) offers fresh momentum towards reaching carbon emissions’ peak and neutrality. Nevertheless, the impact mechanism of the DE on carbon emissions intensity (CEI) is insufficiently characterized. Our study first constructs an expanded comprehensive indicator system to evaluate DE development level from five dimensions containing 17 indicators. Panel data from 30 Chinese provincial regions (2013–2023) were analyzed using fixed effects, mediating effects, and spatial Durbin models to empirically examine the relationship and mechanisms between DE and CEI. Considering the existence of indirect effects of DE on CEs, the mechanism associated with the effect of the DE on CEs from the perspectives of economic growth, industrial structure upgrading, and scientific and technology innovation has been explored. The findings indicate notable regional disparities in the DE level across various provincial regions of China. China’s DE development significantly inhibits CEI. Furthermore, the DE’s development has successfully curtailed CE growth via three mediating mechanisms. And the DE exhibits a critical spatial spillover effect on CEI, and that effect also exhibits regional heterogeneity. Our findings can aid in regional DE development and the creation of policies to reduce CEs. Full article

At a critical juncture in the global low-carbon transition, the role of artificial intelligence (AI) in facilitating low-carbon growth has become increasingly significant. To accelerate the integration of AI with socio-economic development, China has established National New Generation Artificial Intelligence Innovation and Development Pilot Zones (AIPZ). However, the specific impact of these zones on low-carbon development remains unclear. This study utilized panel data from 30 provinces in China from 2013 to 2022 and employed the multi-period difference-in-differences (DID) model and the spatial autoregressive difference-in-differences (SARDID) model to examine the carbon emissions reduction effects of the AIPZ policy and its spatial spillover effects. The findings revealed that the policy significantly reduced carbon emissions intensity (CEI) across provinces, with an average reduction effect of 6.9%. The analysis of the impact mechanism confirmed the key role of human, technological, and financial resources. Heterogeneity analysis indicated varying effects across regions, with more significant reductions in eastern and energy-rich areas. Further analysis using the SARDID model confirmed spatial spillover effects on CEI. This paper aims to enhance understanding of the relationship between AIPZ and CEI and provide empirical evidence for policymakers during the low-carbon transition. By exploring the potential of the AIPZ policy in emissions reduction, it proposes targeted strategies and implementation pathways for policymakers and industry participants to promote the sustainable development of China’s low-carbon economy. Full article

Green public procurement (GPP) is a pivotal policy instrument for advancing urban low-carbon transitions. Using panel data from 285 Chinese cities (2015–2023), this study employs a panel fixed-effects model, mediation analysis, and spatial Durbin model to assess the impact, influencing mechanisms, and spatial spillover effects of GPP on urban carbon emissions intensity. The key findings reveal the following: (1) a 1% increase in GPP implementation is associated with a 1.360% reduction in local urban carbon emissions intensity. (2) GPP reduces urban carbon emissions intensity through urban green innovation, corporate sustainability performance, and public ecological awareness. (3) GPP exhibits significant cross-boundary spillovers, where a 1% reduction in local carbon emissions intensity induced by GPP leads to a 14.510% decline in that in neighboring cities. These results provide robust empirical evidence for integrating GPP into the urban climate governance framework. Furthermore, our findings offer practical insights for optimizing the implementation of GPP policies and strengthen regional cooperation in carbon reduction. Full article

This study investigates the impact of rural digital economy development on agricultural carbon emission efficiency, aiming to elucidate the intrinsic mechanisms and pathways through which digital technology enables low-carbon transformation in agriculture, thereby contributing to the achievement of agricultural carbon neutrality goals. Based on provincial-level panel data from China spanning 2011 to 2022, this study examines the relationship between the rural digital economy and agricultural carbon emission efficiency, along with its underlying mechanisms, using bidirectional fixed effects models, mediation effect analysis, and Spatial Durbin Models. The results indicate the following: (1) A significant N-shaped-curve relationship exists between rural digital economy development and agricultural carbon emission efficiency. Specifically, agricultural carbon emission efficiency exhibits a three-phase trajectory of “increase, decrease, and renewed increase” as the rural digital economy advances, ultimately driving a sustained improvement in efficiency. (2) Industrial integration acts as a critical mediating mechanism. Rural digital economy development accelerates the formation of the N-shaped curve by promoting the integration between agriculture and other sectors. (3) Spatial spillover effects significantly influence agricultural carbon emission efficiency. Due to geographical proximity, regional diffusion, learning, and demonstration effects, local agricultural carbon emission efficiency fluctuates with changes in neighboring regions’ digital economy development levels. (4) The relationship between rural digital economy development and agricultural carbon emission efficiency exhibits a significant inverted N-shaped pattern in regions with higher marketization levels, planting-dominated areas of southeast China, and digital economy demonstration zones. Further analysis reveals that within rural digital economy development, production digitalization and circulation digitalization demonstrate a more pronounced inverted N-shaped relationship with agricultural carbon emission efficiency. This study proposes strategic recommendations to maximize the positive impact of the rural digital economy on agricultural carbon emission efficiency, unlock its spatially differentiated contribution potential, identify and leverage inflection points of the N-shaped relationship between digital economy development and emission efficiency, and implement tailored policy portfolios—ultimately facilitating agriculture’s green and low-carbon transition. Full article

Between 2013 and 2020, China had implemented a pilot cap-and-trade carbon emissions trading system (ETS) in some cities. Previous research has reported that this policy significantly reduces air pollution in the policy-implementing districts. However, whether and to what extent there are spatial spillover effects of this policy on air pollution in other regions has not been sufficiently analyzed. The research objective of this study is to quantitatively assess the spatial spillover effects of China’s carbon ETS on air pollution. Based on data from 288 Chinese cities between 2005 and 2020, this study employs a multiple linear regression approach to estimate the policy effects. Our study finds that the policy significantly reduces the concentrations of black carbon (BC), nitrogen dioxide (NO₂), organic carbon (OC), particulate matter less than 1 micron in size (PM₁), fine particulate matter (PM_2.5), and particulate matter less than 10 microns in size (PM₁₀) in non-ETS regions. This indicates that the carbon ETS has beneficial impacts on air quality beyond the areas where the policy was implemented. The heterogeneity tests reveal that the beneficial spatial spillover effects of the ETS can be observed across cities with different levels of industrialization, population density, economic development, resource endowments, and geographical locations. Further mechanism analyses show that although the policy does not affect the degree of environmental regulation in other regions, it promotes green innovation, low-carbon energy transition, and industrial structure upgrading there, which explains the observed spatial spillover effects. Full article

Under the background of climate change, the risk spillover within the energy system is constantly intensifying. Clarifying the coupling relationship between entities within the energy system can help policymakers propose more reasonable policy measures and strengthen risk prevention. To estimate the risk spillover of energy-related systems, this paper constructs five subsystems: the fossil fuel subsystem, the electricity subsystem, the green bond subsystem, the renewable energy subsystem, and the carbon subsystem. Then, a quantitative risk analysis is conducted on two major energy consumption/carbon emission entities, China and Europe, based on the DCC-GARCH-CoVaR method. The result shows that (1) Markets of the same type often have more significant dynamic correlations. Of these, the average dynamic correlation coefficient of GBI-CABI (the Chinese green bond subsystem) and FR-DE (the European electricity subsystem) are the largest, by 0.8552 and 0.7347. (2) The high correlation between energy markets results in serious risk contagion, and the overall risk spillover effect within the European energy system is about 2.6 times that within the Chinese energy system. Of these, EUA and CABI are the main risk connectors of each energy system. Full article

Facilitating industrial restructuring and modernization plays a pivotal role in realizing China’s dual-carbon objectives (carbon peaking and carbon neutrality) and advancing sustainable socioeconomic progress. Leveraging panel data from 30 provincial-level administrative units (2005–2022) and adopting the Spatial Durbin Model, this research investigates how industrial structure upgrading influences carbon emission intensity within the framework of a unified national market, while elucidating its operational mechanisms. The key findings include the following: (1) Provincial carbon emission intensity demonstrates pronounced “high-high” and “low-low” spatial agglomeration during the study period. Industrial restructuring exhibits marked carbon abatement effects, accompanied by discernible cross-regional spillover benefits. (2) Industrial structure upgrading can reduce carbon emission levels by promoting the technology diffusion effect, while the competitive demonstration effect of digitalization has not yet manifested. (3) The establishment of an integrated national market enhances the capacity of industrial upgrading to suppress carbon emission intensity. (4) The emission-reducing impacts of industrial restructuring manifest heterogeneous patterns across regions and temporal phases: In Eastern China, industrial upgrading paradoxically elevates emission intensity. Central-western regions experience significant emission reductions. Temporally, the relationship follows an inverted U-shaped trajectory. These insights underscore the necessity for policymakers to refine industrial modernization strategies, expedite nationwide market integration mechanisms, and cultivate region-specific green transition roadmaps. Full article

This study investigates the coupling coordination between carbon emission efficiency (CEE) and carbon balance (CB) in the Yellow River Basin (YRB), aiming to support high-quality regional development and the realization of China’s “dual carbon” goals. Based on panel data from 74 cities in the YRB between 2006 and 2022, the Super-SBM model, Ecological Support Coefficient (ESC), and coupling coordination degree (CCD) model are applied to evaluate the synergy between CEE and CB. Spatiotemporal patterns and driving mechanisms are analyzed using kernel density estimation, Moran’s I index, the Dagum Gini coefficient, Markov chains, and the XGBoost algorithm. The results reveal a generally low and declining level of CCD, with the upstream and midstream regions performing better than the downstream. Spatial clustering is evident, characterized by significant positive autocorrelation and high-high or low-low clusters. Although regional disparities in CCD have narrowed slightly over time, interregional differences remain the primary source of variation. The likelihood of leapfrog development in CCD is limited, and high-CCD regions exhibit weak spillover effects. Forest coverage is identified as the most critical driver, significantly promoting CCD. Conversely, population density, urbanization, energy structure, and energy intensity negatively affect coordination. Economic development demonstrates a U-shaped relationship with CCD. Moreover, nonlinear interactions among forest coverage, population density, energy structure, and industrial enterprise scale further intensify the complexity of CCD. These findings provide important implications for enhancing regional carbon governance and achieving balanced ecological-economic development in the YRB. Full article

This paper investigates the impact of carbon emissions trading schemes (CETSs) on agricultural green total factor productivity (AGTFP) using a multi-temporal DID model. Using Chinese prefecture-level city data collected from 2004 to 2022, we find that CETSs enhance AGTFP through technological innovation, with stronger effects in eastern and western regions and positive spillover to neighboring cities. These findings underscore the significant role of CETSs in influencing agricultural productivity and highlight the various factors that contribute to improving AGTFP. Full article

Mitigating traffic-related carbon emissions is pivotal for achieving carbon peaking targets and advancing sustainable urban development. This study employs spatial autocorrelation and high-low clustering analyses to analyze the spatial correlation and clustering patterns of urban road traffic carbon emissions in Shaanxi Province. The spatiotemporal evolution and structural impacts of emissions are quantified through a systematic framework, while the GTWR (Geographically Weighted Temporal Regression) model uncovers the multidimensional and heterogeneous driving mechanisms underlying carbon emissions. Findings reveal that road traffic CO₂ emissions in Shaanxi exhibit an upward trajectory, with a temporal evolution marked by distinct phases: “stable growth—rapid increase—gradual decline”. Emission dynamics vary significantly across transport modes: private vehicles emerge as the primary emission source, taxi/motorcycle emissions remain relatively stable, and bus/electric vehicle emissions persist at low levels. Spatially, the province demonstrates a pronounced high-carbon spillover effect, with persistent high-value clusters concentrated in central Shaanxi and the northern region of Yan’an City, exhibiting spillover effects on adjacent urban areas. Notably, the spatial distribution of CO₂ emissions has evolved significantly: a relatively balanced pattern across cities in 2010 transitioned to a pronounced “M”-shaped gradient along the north–south axis by 2015, stabilizing by 2020. The central urban cluster (Yan’an, Tongchuan, Xianyang, Baoji) initially formed a secondary low-carbon core, which later integrated into the regional emission gradient. By focusing on the micro-level dynamics of urban road traffic and its internal structural complexities—while incorporating built environment factors such as network layout, travel behavior, and infrastructure endowments—this study contributes novel insights to the transportation carbon emission literature, offering a robust framework for regional emission mitigation strategies. Full article

Coordinating development of land urbanization and population urbanization (CDLUPU) to enhance carbon emission efficiency (CEE) is a critical challenge for developing countries experiencing accelerated urbanization. The coupled coordination model and super efficiency SBM are employed to estimate the levels of CDLUPU and CEE across 276 prefecture-level cities from 2010 to 2021. Furthermore, we utilize kernel density estimation and Spatial Durbin Model (SDM) to explore the spatio-temporal distribution characteristics and spatial effects. The results indicate that CDLUPU levels exhibited a sustained upward trend with diminishing regional disparities, whereas CEE displayed a pattern of initial growth followed by decline. Spatial analyses revealed a consistent gradient structure for both CDLUPU and CEE, characterized by radiation decay from southeastern coastal hubs toward interior hinterlands. CDLUPU exerts a significant positive direct impact and spatial spillover effect and indicates that the spillover effects on peripheral regions are substantially stronger than local effects. Regional heterogeneity analysis reveals that CDLUPU negatively affects CEE in eastern China, the Yangtze River Delta (YRD) is more pronounced, but it positively impacts central and western China, as well as Beijing–Tianjin–Hebei (BTH) and Chengdu–Chongqing (CY). Regarding indirect effects, eastern China shows significant positive impact on CEE, and similarly in the YRD. However, central China exhibits a negative effect, whereas BTH shows the opposite trend. Western China and CY show statistically insignificant results. This study offers policy insights for China to coordinate new urbanization strategy and achieve the “dual carbon goal”. Full article

Under carbon emission reduction constraints, accurately assessing the spatial–temporal patterns and drivers of mariculture carbon emissions and sinks is critical for promoting marine economic development and achieving carbon neutrality. This study reviews key components of China’s mariculture carbon and analyzes provincial data from 2008 to 2023 using econometric models to estimate emissions, sinks, and net carbon values. Spatial heterogeneity and spillover effects are examined through geographically weighted regression, Moran’s I, and spatial Durbin models. The findings indicate the following: (1) Both direct and indirect mariculture carbon emissions are rising, with indirect emissions growing faster, notably in Shandong, Fujian, and Guangdong. (2) Shellfish carbon sinks generally dominate; algal carbon sinks are growing rapidly, especially in Fujian, Zhejiang and Shandong. (3) Net carbon values vary by region—positive in Liaoning, Hebei, Shandong, and Hainan, and negative in Jiangsu, Zhejiang, Fujian, Guangdong, and Guangxi. (4) Energy intensity increases emissions; industrial upgrading reduces them. Technological innovation, energy intensity, and ecological constraints enhance sinks. (5) Emission spillovers are positive for energy and negative for structure; sink spillovers are positive for energy and negative for technology; ecological effects are insignificant. Overall, shellfish and algae mariculture play a key role in China’s marine carbon sequestration. Regionalized carbon governance is essential to balance emissions–sinks, and to advance low-carbon mariculture. Full article