Carbon Balance and Management最新文献

The sharing of private charging pile (PCP) can significantly alleviate the construction pressure on public charging infrastructure and benefit for low carbon travel. However, the PCP sharing is in a nascent state with a low market share in reality. Thus, we construct a comprehensive TAM-UTAUT structural equation model to explore the factors influencing the sharing intention of PCP from 660 survey responses of PCP owners. We also analyze the differences across heterogeneous groups. Our finding indicates that perceived trust, performance expectancy, social influence, and incentive policies have a positive impact on the sharing intention of PCP, with incentive policies exhibiting the strongest effect, followed by social influence. Interestingly, female owners' sharing intention is more responsive to both shared revenue and social conformity than that of male owners, whereas male owners tend to have greater concern regarding sharing risks. Younger owner groups are more significantly influenced by the practical effectiveness of sharing, while middle-aged and elderly groups pay more attention to policy incentives and sharing-related risks. Owners without private parking spaces are more influenced by the practical effectiveness of sharing. In contrast, owners with private spaces are more attentive to sharing risks and policy support. Based on the findings, we propose specific recommendations for both the government and the charging service operators to further promote the sharing of PCP.

Developing a fair and effective carbon emissions quotas (CEQ) allocation plan is crucial for China. This study uses the constructed threshold-STIRPAT extended model to predict the carbon peak in China's 30 provinces. Secondly, the entropy-TOPSIS method is used to calculate the initial allocation of CEQ based on the principle of fairness and is assessed through the carbon Gini coefficient. Thirdly, the optimal allocation of CEQ is calculated based on efficiency principle using the ZSG-DEA model. Finally, based on the carbon peak and CEQ, identify the emission reduction pressures faced by provinces. The results indicate that, under the energy-saving development scenario, China carbon emissions (CE) are expected to peak at 11,813.44 Mt by 2030. which can serve as China's overall CEQ; From the perspective of initial allocation of CEQ under the principle of fairness, the initial CEQ in the eastern and central regions are generally higher than those in the western and northeastern regions; From the perspective of optimizing CEQ allocation under the principle of efficiency, the optimized CEQ in Jiangsu, Shandong, and Guangdong are significantly higher than the initial CEQ, while the optimized CEQ in Guangxi and Gansu are significantly lower than the initial CEQ; High-High are mainly concentrated in the northern regions, High-Low are mainly distributed in the central and eastern coastal regions, and Low-Low are mainly distributed in the western and northeastern regions. This study provides a new research approach for developing fair and effective CEQ allocation schemes.

In the context of global climate change, understanding economic efficiency disparities between high-carbon and low-carbon industries is crucial for advancing low-carbon transitions and improving carbon governance. This study examines heterogeneity in corporate carbon emission management and economic performance across Chinese industries and identifies key drivers of firms’ transformation capacity. Using a panel dataset of 633 listed enterprises from eight industries in China over 2010–2021, we classify firms into high- and low-carbon groups based on their emissions profiles and benchmark four machine-learning models—Random Forest, XGBoost, LightGBM, and Decision Tree—to capture nonlinear relationships and evaluate the relative importance of environmental and financial indicators. Random Forest delivers the best performance, achieving a classification accuracy of 95.7% (rounded) and strong discriminatory ability (AUC = 0.989). Feature-importance results consistently show that carbon emissions are the most influential variable, followed by total liabilities and total assets, while profitability-related indicators (e.g., operating revenue and gross profit margin) also contribute to distinguishing firms’ carbon profiles and performance differences. Overall, high-carbon enterprises appear to face greater transition barriers due to higher abatement cost exposure and tighter balance-sheet constraints, whereas low-carbon firms may be better positioned to benefit from policy incentives and market opportunities. These findings highlight the pivotal role of financial health in enabling low-carbon transformation and underscore the need for differentiated policy design. Policy implications include targeted transition finance and more flexible allowance allocation mechanisms for high-carbon enterprises, alongside continued incentives for technological innovation and market expansion in low-carbon sectors.

Q56; G30; C55; Q43; L60

The transition toward low-carbon sustainable development is critical for transforming heavily polluting industries. Green credit policies are designed to direct capital toward environmentally friendly and low-carbon corporates. Using the introduction of China's Green Credit Guidelines in 2012 as a quasi-natural experiment, this study analyzes a panel of A-share listed companies from 2008 to 2021. Employing a Difference-in-Differences approach, we assess the impact of the green credit policy (GCP) on corporate carbon emissions. Empirical results indicate that GCP leads to a significant reduction in corporate carbon emissions. Baseline DID estimates show that treated corporates reduced emissions by approximately 13-19% compared to the control group, a finding that remains robust across a series of checks. The emission-reduction effect of GCP is more pronounced in corporations with a separated board leadership structure, higher profitability, central urban locations, and well-developed digital infrastructure. We identify two primary mechanisms through which GCP operates: imposing financial constraints that deter investment in carbon-intensive activities, and promoting green innovation, which facilitates the adoption of environmentally friendly technologies and practices. Further analysis reveals that both internal governance and external regulatory factors-such as stronger environmental awareness among executives, ISO 14,001 certification, enhanced intellectual property protection, and strict enforcement of the Three Simultaneous System-strengthen the effectiveness of GCP in reducing emissions. Through these channels, GCP supports the transition to a more sustainable economic pathway and contributes to global climate change mitigation.

As the environmental problems caused by the greenhouse effect become more and more serious, and the forest as the largest carbon pool can effectively slow down the greenhouse effect, it is particularly important to accurately predict the carbon storage of the forest. In order to accurately estimate the biomass and carbon storage of Quercus mongolica in Northeast China, the biomass allocation pattern of Q. mongolica was analyzed. In this study, data of 175 Q. mongolica trees in Heilongjiang, Jilin, Liaoning and eastern Inner Mongolia were collected, including aboveground organ biomass, DBH, tree height, age and climatic factors, as well as published carbon content data of different organs. In this study, the biomass allocation pattern of individual Q. mongolica was analyzed. An additively compatible aboveground biomass and carbon storage model and an algebraically controlled aggregation model were established using nonlinear simultaneous equations. After selecting the aggregate biomass compatibility model, climate factors were added to establish a compatibility model containing climate factors. In addition, the root-stem ratio model was used to construct the underground compatible biomass and carbon storage model. The adjusted R²_adj values of the final established aboveground components and aboveground total biomass and carbon storage models were between 0.7048 and 0.9618, the total relative error ( TRE ) was within ± 1%, and the average prediction error ( MPE ) was below 10%, which met the modeling accuracy standard. The belowground biomass models showed adjusted R²_adj values between 0.7702 and 0.7801, TRE ≤ 1%, and MPE < 15%. This study elucidated the biomass allocation pattern of individual Q. mongolica. All the developed models meet the accuracy requirements and can be applied to predict the biomass and carbon storage of Q. mongolica in Northeast China. In the compatibility model with climate factors, the accuracy of leaf and branch models has been greatly improved, indicating that the addition of climate factors in the independent model can greatly improve the accuracy of each component model, which can provide a theoretical basis for the establishment of each component model in the compatibility model of other tree species.