Search Results (340)

Promoting low-carbon travel modes is crucial for China’s transportation sector to achieve the dual carbon goals. When exploring the mechanisms behind individuals’ travel decisions, the relationships between factors such as the built environment and transportation policies are often derived from prior experience or subjective judgment, rather than being grounded in a solid theoretical foundation. In this paper, we build on and integrate the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) by introducing built environment perception (BEP), encouraging policy perception (EPP), and restrictive policy perception (RPP) as either perceived ease of use (PEOU) or perceived usefulness (PU). The integration aims to explain how the latent variables in TPB and TAM jointly affect low-carbon travel intention. We conduct a traveler survey in Shanghai, China to obtain the data and employ a structural equation modeling (SEM) approach to characterize the latent mechanisms. The SEM results show that traveler attitude is the most critical variable in shaping low-carbon travel intentions. Perceived ease of use has a significant positive effect on perceived usefulness, and both constructs directly or indirectly influence attitude. As for transportation policies, encouraging policies are more effective in fostering voluntary low-carbon travel intentions than restrictive ones. Considering the heterogeneity of the traveling population, differentiated policy recommendations are proposed based on machine learning modeling and SHapley Additive exPlanations (SHAP) analysis, offering theoretical support for promoting low-carbon travel strategies. Full article

Industrial structure upgrading serves as an important driving force for the sustained and healthy development of the economy, and it has a positive effect on reducing carbon emission intensity. This study uses provincial panel data from China from 2004 to 2019, starting from the dual perspectives of green total factor productivity and labor misallocation, and employs a four-stage mediation regression model to estimate the mechanism of industrial structure upgrading on carbon emission intensity. The research findings show that: for every 1% increase in industrial structure upgrading, carbon emission intensity will decrease by 0.296%; the central region shows the most significant effect, followed by the western region, while the eastern region shows no significant effect. From the view of the influencing mechanism, industrial structure upgrading will promote green total factor productivity and labor misallocation. When each of the two mediating variables increase by 1%, carbon emission intensity will decrease by 0.12% and 0.054%, respectively. Under the influence of industrial structure upgrading, the inhibitory effects of green total factor productivity and labor misallocation on carbon emission intensity have weakened, and the two factors have made it difficult to form a mediating superposition effect within the sample period. The research conclusion provides the policy implications for China to continuously adhere to industrial structure upgrading, pay attention to improving green total factor productivity, and enhance the low-carbon technical level of workers to achieve the “dual carbon” goals. Full article

As the urbanization and industrialization processes in developing countries continue to advance, environmental issues caused by carbon dioxide emissions (CDEs) have become a significant research topic in the field of sustainable development. However, existing research has primarily focused on macro and meso scales such as global, national, and urban levels, and due to limitations in data precision, in-depth exploration of spatial heterogeneity within cities remains insufficient. To address this, this study utilizes China high-resolution emission gridded data (CHRED) to establish a theoretical analytical framework for spatial zoning of urban carbon emissions. The main innovations of this study are as follows: first, a stepwise analysis method matching carbon emissions with spatial patterns was designed based on CHRED data; second, by establishing a “ring-layer and direction” model, the study systematically revealed the spatial differentiation characteristics of carbon emissions within cities. Empirical research using Shenzhen as a case study shows that the city’s CDE intensity (CDEI) is generally at a medium-to-low level, but exhibits significant spatial heterogeneity, with Nanshan District and Kuiyong District forming two major high-emission core areas. Further analysis reveals that during the processes of urbanization and industrialization, population density, nighttime light intensity index, and the proportion of construction land are the key drivers influencing the spatial pattern of carbon emissions. This study provides scientific basis and decision-making references for optimizing urban spatial layout to achieve the “dual carbon” goals. Full article

Achieving carbon reduction is essential in advancing China’s dual carbon goals and promoting a green transformation in the transportation sector. Changes in energy structure and intensity constitute key drivers for sustainable and low-carbon development in this field. To explore the spatial spillover effects of the energy structure and intensity on the green transition of transportation, this study constructs a panel dataset of 30 Chinese provinces from 2007 to 2020. It employs a super-efficiency SBM model, non-parametric kernel density estimation, and a spatial Markov chain to verify and quantify the spatial spillover effects of green total factor productivity (GTFP) in the transportation sector. A dynamic spatial Durbin model is then used for empirical estimation. The main findings are as follows: (1) GTFP in China’s transportation sector exhibits a distinct spatial pattern of “high in the east, low in the west”, with an evident path dependence and structural divergence in its evolution; (2) GTFP displays spatial clustering characteristics, with “high–high” and “low–low” agglomeration patterns, and the spatial Markov chain confirms that the GTFP levels of neighboring regions significantly influence local transitions; (3) the optimization of the energy structure significantly promotes both local and neighboring GTFP in the short term, although the effect weakens over the long term; (4) a reduction in energy intensity also exerts a significant positive effect on GTFP, but with clear regional heterogeneity: the effects are more pronounced in the eastern and central regions, whereas the western and northeastern regions face risks of negative spillovers. Drawing on the empirical findings, several policy recommendations are proposed, including implementing regionally differentiated strategies for energy structure adjustment, enhancing transportation’s energy efficiency, strengthening cross-regional policy coordination, and establishing green development incentive mechanisms, with the aim of supporting the green and low-carbon transformation of the transportation sector both theoretically and practically. Full article

Karst regions, characterized by thin soil layers, severe rocky desertification, and fragile vegetation, hold significant scientific value for achieving China’s “dual-carbon” goals. This study focuses on Zhijin County in Guizhou Province, integrating provincial carbon density data with forest resource inventory data. By constructing a model to adjust aboveground forest carbon density (AGC) estimation parameters and utilizing the InVEST model alongside hotspot analysis, the research systematically examines the spatiotemporal heterogeneity of carbon storage from 2000 to 2020. These findings provide actionable strategies for enhancing carbon sequestration efficiency in ecologically fragile regions, supporting China’s “dual-carbon” policy goals. Key findings include: (1) Carbon storage exhibits a “growth-turning point” two-phase pattern, increasing by 0.46% from 2000 to 2015 but decreasing by 3.31% in 2020 due to construction land expansion. (2) There are significant differences in carbon storage among ecological engineering projects, with the highest carbon storage found in the “Grain-for-Green Program” project area and the lowest in the “National Rocky Desertification Control Program” area. (3) Elevation is the primary controlling factor for carbon storage, with rocky desertification showing notable spatial differentiation. This study provides theoretical support for the precise regulation of ecological programs and the development of high-precision carbon storage models in karst regions. Full article

Land use change significantly affects regional carbon emissions and ecosystem service value (ESV). Under China’s Dual Carbon Goals, this study takes Beijing-Tianjin-Hebei, experiencing rapid land use change, as the study area and counties as the study unit. This study employs a combination of methods, including carbon emission coefficients, equivalent-factor methods, bivariate spatial autocorrelation, and a multinomial logit model. These were used to explore the spatial relationship between land use carbon emissions and ESV, and to identify their key driving factors. These insights are essential for promoting sustainable regional development. Results indicate the following: (1) Total land use carbon emissions increased from 2000 to 2015, then declined until 2020; emissions were high in municipal centers; carbon sinks were in northwestern ecological zones. Construction land was the primary contributor. (2) ESV declined from 2000 to 2010 but increased from 2010 to 2020, driven by forest land and water bodies. High-ESV clusters appeared in northwestern and eastern coastal zones. (3) A significant negative spatial correlation was found between carbon emissions and ESV, with dominant Low-High clustering in the north and Low-Low clustering in central and southern regions. Over time, clustering dispersed, suggesting improved spatial balance. (4) Population density and cultivated land reclamation rate were core drivers of carbon–ESV clustering patterns, while average precipitation, average temperature, NDVI, and per capita GDP showed varied effects. To promote low-carbon and ecological development, this study puts forward several policy recommendations. These include implementing differentiated land use governance and enhancing regional compensation mechanisms. In addition, optimizing demographic and industrial structures is essential to reduce emissions and improve ESV across the study area. Full article

In the context of global warming and China’s “dual carbon” goals, Shanxi, as China’s main coal-producing region (accounting for 28.4% of the country’s coal production), is facing the dual challenges of carbon emission reduction and economic development. Based on the data from 1990 to 2019, this study quantitatively analysed the carbon emission driving mechanisms of seven major energy sources in Shanxi, including coal, coke, and gasoline, through the coupling analysis of the Kaya identity and the LMDI model, and explored the climate change mitigation pathways. The results show that the total carbon emissions of Shanxi’s energy sector increased significantly from 1990 to 2019, with coal being the most important emission source. Through the decomposition of the LMDI model, it is found that the effect of economic activity is the core driving force of carbon emission growth, and the improvement of energy intensity is the key inhibitor. It is worth noting that the demographic effect turned negative after 2010, which had a dampening effect on the growth of carbon emissions. In addition, the adjustment of energy structure shows the characteristics of stages: the structural effect of coal has turned from negative to positive after 2010, while the proportion of clean energy, such as natural gas, has increased, indicating that the optimisation of energy structure has achieved initial results. Based on the above findings, the study proposes three major paths for climate mitigation in Shanxi’s energy industry: (1) promote low-carbon upgrading of the industry and reduce the economy’s dependence on high-carbon energy; (2) Strengthen energy efficiency and continuously reduce energy consumption per unit of GDP through technological innovation; (3) accelerate the transformation of the energy structure and expand the proportion of clean energy such as natural gas and renewable energy. This paper innovatively provides an empirical reference for the model-based, coupling-based carbon emissions-driven analysis and climate mitigation strategy design in resource-based areas. Full article

As a pivotal engine of global economic growth, the digital economy provides nations with new momentum to achieve carbon neutrality. By driving inter-industry mobility and reallocation of production factors, the digital economy alters industrial agglomeration patterns, which ultimately influence carbon emissions. Understanding the intrinsic mechanisms through which the digital economy affects carbon emissions is therefore critical for both theoretical and practical significance in advancing green and low-carbon development. This study employs panel data from 278 Chinese cities (2011–2020) to investigate the mechanism by which the digital economy affects urban carbon emissions from the perspective of industrial agglomeration. Our findings indicate that the development of the digital economy significantly reduces urban carbon emissions; a one-percentage-point increase in digital economy development leads to a 0.091% decline in carbon emission intensity. Contrary to conventional expectations, however, higher levels of industrial agglomeration do not contribute to carbon reduction. Mediation analysis reveals that the digital economy enhances industrial agglomeration, which in turn weakens its direct carbon mitigation effect by approximately 6%. Furthermore, the impact varies across regions, city sizes, and industry sectors. These insights offer valuable policy implications for China’s digital transformation, industrial agglomeration optimization, and energy-saving strategies to achieve its dual carbon goals. Full article

Against the backdrop of intensifying global climate change and advancing the goal of the “dual-carbon” strategy, the built environment is being viewed as a complex socio-technical system in which technological, economic, demographic and institutional subsystems are coupled and evolving at different scales. As a core node in this system, residential buildings not only carry infrastructural functions, but are also deeply embedded in energy flows, material cycles and behavioural structures, which have a significant impact on carbon emissions. Given the high volume of residential buildings in China and the significant differences between urban and rural construction, there is an urgent need to systematically identify and analyse the implicit carbon emissions during the materialisation phase. In this paper, from the perspective of systems engineering, we selected 30 urban and rural residential buildings in provinces and cities from 2005 to 2020 as the research objects, adopted the life cycle assessment (LCA) method to account for the implied carbon emissions in the materialisation stage, and systematically identified the driving factors of carbon emissions based on the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. From this study, we made the following conclusions: (1) the total carbon emissions of residential buildings in urban and rural areas in China continue to rise during the materialisation stage, showing a spatial pattern of “high in the south-east and low in the north-west”, with a significant trend of structural transformation in urban and rural areas and with steel–concrete structures dominating in towns and cities, and bricks and steel being used in rural areas. (2) Resident population and disposable income are generally positive driving factors, while the influence of industrial structure and energy intensity is heterogeneous between urban and rural areas. For overall residential buildings, every 1% increase in resident population and income will lead to a 1.055% and 0.73% increase in carbon emissions, respectively. The study shows that life-cycle-oriented carbon accounting and the identification of multidimensional driving mechanisms are of great policy value in developing urban–rural differentiated emission reduction paths and enhancing the effectiveness of carbon management in the building sector. Full article

Making full utilisation of the synergies that exist among the various stages of industrial green production is beneficial to the realisation of the dual-carbon goal. However, the synergistic effects among the three stages of industrial green production have not yet been explored in depth from a microscopic perspective. Based on the analytic hierarchy process, the entropy weighting method, the coupled synergy degree model, the spatial autocorrelation test, and the geographically weighted regression model (GWR), the spatiotemporal evolution characteristics and driving mechanism of the synergistic effects among the three stages of industrial green production were explored by utilising the relevant data of industrial enterprises in 30 provinces of China from 2012 to 2022. The results showed that the synergistic effect of industrial green production exhibited an upward trend over time, and displayed a regional distribution characteristic of decreasing from east to west. The spatial differences in the synergistic effects of industrial green production gradually narrowed and the number of provinces with high–high (H-H) agglomerations increased. The level of digital economy development, the urbanisation level, the optimisation of industrial structure, the level of green credit, and the intensity of environmental regulation were the main driving factors for the synergistic effects of industrial green production, and there were significant spatial differences. This study provides a basis for the formulation of differentiated regional green development policies from the perspective of synergizing the various stages of industrial green production. Full article

Carbon emission reduction serves as a pivotal strategy for advancing global environmental quality and sustainable socioeconomic development. Private enterprises serve as the primary contributors to industrial carbon emissions. Their low-carbon transition is directly tied to the achievement of China’s Dual Carbon Goals. However, constrained by market failures and the profit-driven nature of capital, these enterprises face significant challenges in both motivation and capacity for carbon emission reduction. As a critical link connecting government and market forces, whether state-owned capital can effectively drive private enterprises to reduce emissions and conserve energy still lacks systematic empirical evidence. Leveraging a panel dataset of private industrial listed companies on China’s Shanghai and Shenzhen A-share markets spanning 2008–2022, we examine the impact of state-owned capital participation on carbon emission reduction and the underlying mechanisms. The empirical results demonstrate that state-owned capital participation can significantly drive carbon emission reduction and propel the low-carbon transformation of private enterprises. Mechanism analysis reveals that state-owned capital participation promotes carbon emission reduction through multiple avenues, including enriching the green resource base, strengthening the value recognition of environmental social responsibility, and improving energy efficiency. Further analysis indicates that the emission reduction effect of state-owned capital participation is more pronounced under conditions of weaker government environmental regulation, lower regional marketization, greater industry competition, and tighter green financing constraints. This study enriches the research on mixed-ownership reform and low-carbon transition of enterprises, deepens the theoretical understanding of the internal mechanism of state-owned capital participation affecting carbon emission reduction, and offers empirical evidence for emerging economies to address the dilemma of emission reduction through property rights integration. Full article

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

Oil camellia (Camellia oleifera) residues from low-yield forests offer significant potential for carbon emission reductions across multiple product pathways—fibreboard, pulp, and bioelectricity. Life cycle assessments (LCAs) were conducted for these three products, revealing distinct carbon footprints driven by energy use, chemical inputs, and combustion processes. Fibreboard production showed a carbon footprint of 244.314 kg CO₂e/m³, primarily due to diesel use and electricity consumption. Pulp production exhibited the highest carbon intensity at 481.626 kg CO₂e/t, largely driven by chemical consumption and fossil fuels. Bioelectricity, with the lowest carbon footprint of 41.750 g CO₂e/kWh, demonstrated sensitivity to transportation logistics and fuel types. Substitution and scenario analysis showed that emission reductions can be achieved by optimizing energy structure, substituting high-carbon chemicals, and improving transportation efficiency. The findings highlight the substantial reduction potential when oil camellia residues replace conventional feedstocks in these industries, contributing to the development of low-carbon strategies within the bioeconomy. These results also inform the design of targeted mitigation policies, enhancing carbon accounting frameworks and aligning with China’s dual-carbon goals. Full article

Under the framework of the “dual-carbon” goals of China (“carbon peak” by 2030 and “carbon neutrality” by 2060), the escalating complexity of emerging power systems presents significant challenges to safety governance. Traditional management models are now confronting bottlenecks, notably in knowledge inheritance breakdown and lagging risk prevention and control. This paper explores the application of large AI models in safety and emergency management in the power industry. Through core capabilities—such as natural language processing (NLP), knowledge reasoning, multimodal interaction, and auxiliary decision making—it achieves full-process optimization from data fusion to intelligent decision making. The study, anchored by 18 cases across five core scenarios, identifies three-dimensional challenges (including “soft”—dimension computing power, algorithm, and data bottlenecks; “hard”—dimension inspection equipment and wearable device constraints; and “risk”—dimension responsibility ambiguity, data bias accumulation, and model “hallucination” risks). It further outlines future directions for large-AI-model application innovation in power industry safety and management from a four-pronged outlook, covering technology, computing power, management, and macro-level perspectives. This work aims to provide theoretical and practical guidance for the industry’s shift from “passive response” to “intelligent proactive prevention”, leveraging quantified scenario-case analysis. Full article

Driven by the “dual carbon” goal, virtual power plants (VPPs) are the core vehicle for integrating distributed energy resources, but the multiple uncertainties in wind power, electricity/heat load, and electricity price, coupled with the impact of carbon-trading cost, make it difficult for traditional scheduling methods to balance the robustness and economy of VPPs. Therefore, this paper proposes an oxy-fuel combustion capture (OCC)-VPP architecture, integrating an OCC unit to improve the energy efficiency of the system through the “electricity-oxygen-carbon” cycle. Ten typical scenarios are generated by Latin hypercube sampling and K-means clustering to describe the uncertainties of source and load probability distribution, combined with the polyhedral uncertainty set to delineate the boundary of source and load fluctuations, and the stepped carbon-trading mechanism is introduced to quantify the cost of carbon emission. Then, a three-stage stochastic–robust scheduling model is constructed. The simulation based on the arithmetic example of OCC-VPP in North China shows that (1) OCC-VPP significantly improves the economy through the synergy of electric–hydrogen production and methanation (52% of hydrogen is supplied with heat and 41% is methanated), and the cost of carbon sequestration increases with the prediction error, but the carbon benefit of stepped carbon trading is stabilized at the base price of 320 DKK/ton; (2) when the uncertainty is increased from 0 to 18, the total cost rises by 45%, and the cost of purchased gas increases by the largest amount, and the cost of energy abandonment increases only by 299.6 DKK, which highlights the smoothing effect of energy storage; (3) the proposed model improves the solution speed by 70% compared with stochastic optimization, and reduces cost by 4.0% compared with robust optimization, which balances economy and robustness efficiently. Full article