Carbon Balance and Management最新文献

Amid China’s sustained medium-high speed economic growth, the extensive development model has caused severe climate deterioration and environmental pollution. The implementation of low-carbon city pilot policies provides a strategic direction for the sustainable development of agribusinesses. Using unbalanced panel data from 781 agriculture-related listed enterprises in China between 2007 and 2023, this study employs the PSM-DID model to estimate the average treatment effect and heterogeneity of low-carbon city pilot policies. In addition, a mediation model is applied to examine the mechanism through which digital transformation enhances the Environmental-Social-Governance (ESG) performance of these enterprises under policies. The empirical results indicate that: (1) low-carbon city pilot policies simultaneously improve the environmental, social, and governance performance of agribusiness enterprises, thereby enhancing their overall ESG performance. (2) The impact of policies on ESG, environmental, social, and governance performance varies across regions, with the western region showing the most significant improvement in ESG performance. (3) Digital transformation plays a significant mediating role in the effect of low-carbon city pilot policies on ESG, environmental, social, and governance performance. Based on these findings, agribusiness should leverage policy advantages to enhance sustainable development through multi-dimensional performance. The government should introduce targeted policies to help enterprises define development directions and formulate localized strategies. Furthermore, agribusinesses must strengthen digital transformation as a core driver for improving ESG performance.

In the context of carbon peaking and neutrality goals, combined with the pursuit of high-quality agricultural economic development, examining the spatial disparities and convergence of agricultural green and low-carbon transformation is critical for protecting the ecological environment and enhancing national agricultural ecological security. This study estimates agricultural carbon emissions across five dimensions: farmland use, rice cultivation, livestock production, farmland soils, and crop residue burning. Using the EBM-GML model, the study measures the agricultural green and low-carbon transformation index for 30 Chinese provinces from 2005 to 2023. The Dagum Gini coefficient, standard deviation ellipses, kernel density estimation, and spatial Durbin models are employed to analyse the spatial disparities and convergence of China’s agricultural green and low-carbon transformation. The findings reveal that the level of agricultural green and low-carbon transformation across provinces and the three major economic zones has increased over time, showing a spatial pattern of “high levels at the periphery and low levels at the centre,” with inter-zonal disparities gradually widening. Regional dynamics in this transformation vary significantly, with northern regions growing faster than southern regions. Nationally, the transformation expanded markedly over the sample period, accompanied by growing divergence within the eastern region. At both the national and major sub-regional levels, the agricultural green and low-carbon transformation shows no δ-convergence but demonstrates absolute and conditional β-convergence. This suggests that although regional divergence in the agricultural green and low-carbon transformation does not consistently decline over time, areas with higher transformation levels experience faster reductions than those with lower levels. Consequently, the gap between the two groups gradually narrows, ultimately converging toward a common steady-state level.

Carbon sequestration (CS), a key component of climate change mitigation and carbon neutrality efforts, is strongly influenced by land use/land cover change (LUCC). However, the spatiotemporal evolution of CS in response to future LUCC trajectories under urbanization remains underexplored. Therefore, this study focuses on the Yangtze River Economic Belt (YREB), a region characterized by rapid urbanization and high carbon emissions, and develops an integrated SD-PLUS-InVEST modelling framework to evaluate the impacts of LUCC under three SSP-RCP scenarios on regional CS dynamics. Results show that, cropland is projected to decline by 5%, 10%, and 3%, while forestland increases by 8%, 3%, and 5% under SSP1-2.6, SSP5-8.5, and SSP2-4.5, respectively. Correspondingly, CS shows a 12% increase under SSP1-2.6, an 8% decrease under SSP5-8.5, and a moderate enhancement under SSP2-4.5. The XGBoost-SHAP analysis quantified the impacts of key drivers, revealing that elevated temperature and population growth are strongly correlated with declines in CS, while economic growth is positively correlated with enhanced CS capacity. This research provides valuable insights into how LUCC affects CS under varying development scenarios, offering actionable guidance for formulating regional land-use policies that promote CS and contribute to climate mitigation.

The climate system is undergoing unprecedented and dramatic changes, with increasing frequency and intensity of extreme events such as heat waves, droughts and heavy rainfall. Climate change has triggered profound changes in the global carbon cycle and eco-hydrological processes, posing unprecedented challenges for watershed carbon management, and quantifying climate-driven eco-hydrological processes remains critical for achieving watershed-scale carbon neutrality. In this study, we developed an integrated modeling framework combining Biome-BGC, GBHM and RWEQ models, aiming to comprehensively assess the ecohydrological processes and carbon cycle changes in the west liao River Basin (WLRB). Our results suggest that the future climate of the WLRB (1991–2100) will shift towards a warmer and wetter climate, accompanied by decreasing wind speeds but increasing extreme wind events. These changes drive three key carbon-climate feedbacks: warmer maximum temperatures lead to degradation of vegetation productivity in the plains, weakening watershed carbon sequestration capacity and reducing the sensitivity of vegetation to precipitation in the semi-arid zone. Increased frequency of extreme wind speeds greatly increases the potential for wind erosion in the WLRB, threatening soil organic carbon storage. From the perspective of aquatic carbon pools, despite reduced drought risk and increased water availability, there is a strong likelihood of increased frequency and intensity of flooding, which may exacerbate lateral carbon export. Our findings highlight that climate change amplifies synergistic risks to terrestrial and aquatic carbon pools, requiring adaptive strategies such as establishing synergistic vegetation restoration models that integrate windbreak-carbon sequestration with flood regulation. These findings not only improve our understanding of the evolutionary mechanisms and potential risks of ecohydrological processes, but also provide guidance for future watershed carbon management.

Background

As China restructures its energy landscape through transition, environmental monitoring serves as vital technological infrastructure. It directly supports the achievement of carbon neutrality objectives. This paper uses the number and density of environmental monitoring stations established around industrial enterprises as proxy variables for environmental supervision and the China Industrial Enterprise Pollution Emission Database. It explores the impact of regional environmental monitoring on enterprise reduction of pollution and carbon emissions based on the regulatory and geographic structure.

Results

The empirical results of this paper indicate that (1) Environmental monitoring significantly reduces firms’ SO₂ emissions and emission intensity, and also has a marked abatement effect on other pollutants such as exhaust gases and industrial smoke (dust) particulates; (2) The decline in emissions occurs mainly through three channels: reducing the scale of energy use, lowering energy intensity, and promoting production technology upgrades; (3) Environmental monitoring exhibits a significant co-benefit of pollution reduction and carbon mitigation, decreasing not only pollutant emissions but also the total volume and intensity of CO₂ emissions.

Conclusion

This study demonstrates that environmental monitoring can effectively drive enterprises to reduce energy consumption, enhance technological innovation, and achieve coordinated pollution and carbon reduction. Policy implications suggest that strengthening monitoring networks and adopting differentiated regulatory mechanisms are crucial for promoting energy efficiency, fostering green innovation, and advancing the realization of China’s “dual carbon” goals.

Against the backdrop of intensifying climate change and air pollution, Clean Air Policy (CAP) and the New Energy Demonstration City (NEDC) pilot policy serve as foundational pillars of China’s strategy to realize energy transition (ET). While the individual effects of each policy have been extensively studied, their potential superposition effect on ET remains unexplored. To investigate this superposition effect, we employ panel data covering 278 Chinese cities from 2010 to 2021. Our baseline regression results demonstrate a positive superposition effect of the two policies in promoting ET. The findings remains robust after a series of robustness tests. Mechanism analysis reveals that energy consumption reduction, industrial structure upgrading, and energy efficiency improvement are three transmission channels. Moreover, we find that the superposition of the two policies on ET exhibits heterogeneity across regions, urban scales, levels of economic development, resource endowments, environmental regulation intensity, and officials’ promotion pressure, highlighting how local conditions shape policy effectiveness. These findings shed new light on the logical nexus among policy design, impact, and mechanism in this field, offering both theoretical insights and policy guidance for economies striving to achieve similar multi-dimensional goals.

Background

The United States Forest Service Forest Inventory and Analysis (FIA) database is extensive and complex, requiring significant processing to link its multiple tables. In addition, integrating FIA data with external datasets, such as climate data and remote sensing products, involves substantial preprocessing and computational effort, posing challenges for users without advanced programming expertise.

Results

To efficiently process, analyze, and visualize forest attributes using the FIA database, we developed PyFIA, an open-source Python-based tool. PyFIA provides a suite of functions, including statistical analyses, spatial mapping, and a bookkeeping model for tracking forest biomass dynamics at different scales. Additionally, it can acquire climate information for each inventory plot, enabling in-depth investigations of how climate conditions influence the spatial and temporal patterns of forest attributes.

Conclusions

This program enhances the use of FIA inventory data in forest related studies, particularly for forest carbon. It also incorporates raster datasets, providing a valuable resource for research on forest ecosystems. PyFIA is designed with a modular structure and is openly available on GitHub, enabling easy access, customization, and continuous improvement. Users can contribute to its development, ensuring long-term sustainability. In addition, its flexible architecture allows for the integration of new functions, making it highly adaptable to diverse research needs.

Climate change has intensified global demands for industrial decarbonization and carbon neutrality. As the world’s largest carbon emitter, China’s policy approach is pivotal to international climate governance and the low-carbon transition. This study conducts the first systematic evaluation of China’s industrial decarbonization policy framework established toward the carbon neutrality goal. Through a mixed-methods approach combining bibliometric analysis and Policy Modeling Consistency (PMC) Index, we analyze 58 national policy documents comprising approximately 610,000 Chinese characters. Results across five key decarbonization pathways show notable disparities in policy consistency: carbon emission abatement achieves perfect consistency (PMC-Index = 9.07), reflecting China’s prioritization of greenhouse gas emission controls, while energy efficiency (8.14) and scientific and technological innovation (8.12) demonstrate good consistency. By contrast, socio-economic risk mitigation (6.97) and circular economy (6.77) pathways only reach acceptable levels, revealing gaps in integrating just transition principles and industrial symbiosis. The asymmetric consistency stems from a misalignment across the five policy pathways, particularly the underdeveloped linkages between decarbonization, circularity, and socio-economic consideration. We recommend strengthening circular economy institutions through sector-specific material flow governance and industrial symbiosis networks, alongside proactive just transition policies such as skill development initiatives and compensatory mechanisms for vulnerable communities. This study contributes to theories of environmental governance and policy mixes, while offering globally applicable insights for reconciling emission reduction with industrial competitiveness and social equity.

The development of a low-carbon economy is essential for current economic growth. To gain a comprehensive understanding of the spatiotemporal evolution of low-carbon economy development in China’s Yangtze River Delta and to clarify its influencing factors, this research employs spatial analysis methods such as kernel density estimation, Moran’s I, spatial Markov chains, and the spatiotemporal geographically weighted regression model. The study draws the following conclusions: (1) The overall development of the low-carbon economy in the region is improving, though there are notable spatial and temporal differences between cities. In recent years, the number of cities with high levels of low-carbon development has steadily increased, yet provincial disparities remain, with Anhui Province facing the most prominent development challenges. (2) Low-carbon economy development in the Yangtze River Delta shows clear spatial clustering and migration patterns. The number of cities in a low-low agglomeration state remains stable, while the number of high-high agglomeration cities has slightly decreased. Spatially, cities with high-high agglomeration are concentrated in the eastern part of the region, while low-low agglomeration is mainly found in the west. (3) Various factors influence the development of the low-carbon economy in the Yangtze River Delta. The average regression coefficients for industrial upgrading, government intervention, technological advancement, education, and economic development are all positive, the effect of human capital shows stage-specific characteristics.

Background

China is a major carbon emitter in the construction industry and the world’s second-largest economy. Clarifying the scientific relationship between carbon emissions and economic development has prominence realistic meaning for China and even other developing countries to carry out more effective carbon emission reduction work in the construction industry.

Results

This study combines the Tapio decoupling model, EKC theory and grey correlation analysis to study the relationship between carbon emissions and total output value of the construction industry in thirty provinces of China. The results show that most areas of central and eastern China have basically achieved weak decoupling, while other regions are not stable enough. In addition, in 2022, 18 regions meet the inverted U-shaped curve, and the overall is on an upward trend; Beijing, Hebei and Sichuan have passed the peak of the curve. The national construction industry is in expansion connection (decoupling index is 0.92), showing that the development of the industry is tending towards a coordinated state.

Conclusion

There is still a lot of room for China’s construction industry to reduce carbon emission. Each region can refer to the evolution law of decoupling state obtained in this paper, and formulate more efficient carbon reduction measures according to local conditions, which helps the industry achieve green and sustainable development.