Carbon Balance and Management最新文献

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.

Escalating environmental challenges necessitate accelerated green technology innovation and diffusion. This paper introduces a novel theoretical framework synthesizing differential game theory, endogenous growth with directed technical change, and network analysis to investigate the interplay between strategic national R&D investments, the endogenous direction of innovation, and structured international knowledge spillovers. The model contrasts non-cooperative and cooperative equilibria, revealing that non-cooperation yields suboptimal global outcomes: underinvestment in green R&D, delayed transitions, and a failure to curb long-term pollution, driven by free-riding on environmental benefits and knowledge spillovers. The spillover network’s architecture critically mediates these dynamics. Conversely, cooperative solutions markedly improve environmental and technological trajectories. Numerical simulations confirm these findings and demonstrate that globally coordinated policy mixes, specifically carbon pricing combined with green R&D subsidies, can effectively approach cooperative outcomes. The analysis underscores the critical roles of relative green R&D productivity and spillover intensity in determining the pace and success of the global green transition. This research provides a comprehensive lens for understanding and shaping the global green technology landscape and formulating effective international environmental and technology policies.

Background

Understanding the spatial effects of the synergistic development between agricultural carbon sequestration and emission reduction (ACSER) and food security (FS) is essential for promoting sustainable and high-quality agrarian development. Based on a constructed indicator system for ACSER and FS, this study measures the Coupling Coordination Degree (CCD) between the two in different grain functional areas of China from 2000 to 2023 using a modified coupling synergy model. Furthermore, a Spatial Durbin Model (SDM) is employed to explore the underlying driving mechanisms and spatial spillover effects of their synergistic relationship.

Results

(1) From 2000 to 2023, China’s CCD between ACSER and FS exhibited a clear spatial gradient, higher in the north and lower in the south. Specifically, spatial clustering intensified in the main production and production-marketing areas but did not reach statistical significance, while the main marketing regions consistently exhibited a trend of spatial dispersion; (2) The spatial Durbin model analysis reveals that the CCD of ACSER and FS in China exhibits significant spatial spillover effects. Among the influencing factors, CO₂ uptake by major crops emerges as the primary driver of their synergistic development, while a higher proportion of cultivated land and increased pesticide and fertilizer use exert negative effects on both local and neighboring regions. (3)The study of regional heterogeneity shows that the CO₂ absorption of agricultural crops in the main production area promotes synergistic development; the synergistic mechanism of ACSER and FS in the production and marketing areas is more complicated, and resource mismatch is the main factor affecting the coupling and synergism of the two; The total power of agricultural machinery in the main marketing areas has a catalytic effect on the local synergy between ACSER and FS, while transregional transmission exhibits negative spillovers, highlighting resource allocation imbalances.

Conclusions

Therefore, to promote the synergistic development of ACSER and FS, it is necessary to adopt region-specific measures based on spatial differences and to strengthen the synergistic effects among key factors within the agricultural system, to enhance resource allocation efficiency and system resilience.

The biogeochemical cycling of carbon in aquatic systems is profoundly regulated by extreme hydrological events, particularly through their impacts on dissolved carbon species (DCs) and total ammonia nitrogen (TAN). Despite growing recognition of these interactions, the spatial correlations and environmental linkages between DCs and TAN during meteorological extremes remain poorly constrained in large river systems. To address this critical uncertainty, we conducted a field campaign during the unprecedented summer drought (June–September 2022) in the Changjiang River Basin, collecting 24 water samples across three lateral positions along the upper Changjiang River mainstem. Our analyses revealed three key findings: First, dissolved inorganic carbon (DIC) constituted the predominant DC component (> 75%), while dissolved organic carbon (DOC) exhibited marked spatial variability (coefficient of variation > 35%). Second, bank-specific correlations emerged between carbon fractions and TAN, with DCs-TAN relationships showing strong correlations along river banks but no significant association in the river center. Third, spatial autocorrelation analyses using univariate and bivariate Moran’s I indices quantified these heterogeneities, particularly revealing a striking positive association between DOC and TAN in the right bank (Moran’s I = 0.64). This spatial variability suggests synergistic controls by drought-induced hydrological forcing, land-use derived inputs, and water quality parameters. Our findings establish a mechanistic framework linking extreme drought conditions to lateral carbon-nutrient coupling patterns, providing critical baseline data for modeling climate-driven biogeochemical shifts in monsoon-regulated river systems.