Carbon Balance and Management最新文献

Background

Against the backdrop of deep specialization within global value chains (GVCs), it is crucial to explore how firms’ participation in global production networks affects their carbon efficiency, a key factor in achieving green growth. Using merged data from the Chinese National Tax Survey Database, the Chinese Customs Trade Statistics Database between 2008 and 2014, and the World Input-Output Database, this paper empirically examines the effect of firms’ position embedded in GVCs on carbon emission efficiency in China’s manufacturing sectors.

Results

It is found that: (1) Improving firms’ position embedded in GVCs can significantly improve their carbon emission efficiency. (2) This improvement is primarily driven by trade structures optimization and technological innovation. (3) Forward GVCs embeddedness exerts a stronger positive impact on carbon efficiency compared to backward embeddedness. And the carbon efficiency benefits of upgrading to higher positions within GVCs are more pronounced in firms with a higher degree of participation, those engaged in mixed and general trade, firms in high-pollution industries, and those located in non-resource-oriented cities. (4) Participation in GVCs contributes to energy conservation and emission reduction, supporting long-term low-carbon and intensive development of enterprises.

Conclusions

The findings shed light on the crucial role of GVCs embeddedness in enhancing carbon emission efficiency, offering a solid foundation for understanding how globalization contributes to achieving long-term sustainable development goals.

Forest carbon projects hold significant potential for mitigating greenhouse gas emissions. However, growing scrutiny has raised concerns about their climate integrity, particularly the gap between scientific knowledge and the practical implementation of carbon quantification methodologies. Southeast Asia, a rainforested tropical region, is a key focus for the development of forest carbon projects. This study critically reviewed the quantification methods and associated reporting of 69 forest carbon projects across Southeast Asia, guided by three essential and interrelated criteria: transparency, robustness, and consistency. The findings reveal limited disclosure in methodological reporting, the adoption of potentially unreliable quantification practices, and substantial variability due to the differing standards adopted by projects. These issues risk undermining the credibility of carbon credits and may hinder their alignment with national and international climate goals. By identifying key methodological gaps and proposing clear evaluation criteria, this study contributes to ongoing debates around forest carbon credit integrity and underscores the urgent need for more transparent, rigorous, and standardised carbon accounting practices within the sector.

Accurate carbon price prediction can help the government establish an effective and stable carbon trading market mechanism, which researchers are increasingly focusing on. However, much research on carbon price prediction has ignored the impacts of multiple factors on the carbon price. A novel ensemble deep learning prediction model, termed CEEMDAN-Attention-RNN, which considers multiple influencing factors, has been proposed to improve the accuracy of carbon price prediction. Firstly, raw data such as carbon price and external variables are decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) into multiple sub-components with different frequencies. Then, the recurrent neural network (RNN) enhanced by LSTM and GRU is combined with the attention mechanism to form a prediction model. Finally, several evaluation indicators are used to obtain the final prediction accuracy, and the model is applied to 3 pilot areas of carbon trading in the Yangtze River basin. The results indicate that the mean absolute percentage errors of the proposed model are 1.8872%, 1.5686%, and 5.2548% in Shanghai Municipality, Hubei Province, and Chongqing Municipality, respectively, and its forecasting ability is better than that of other carbon price forecasting models. Therefore, the proposed model is an excellent method for carbon trading price prediction due to its high accuracy. In addition, high-precision carbon trading price forecasting technology is of great significance for the government to formulate emission reduction policies.

Optimizing the spatial pattern of its carbon storage is of great significance for increasing the carbon storage capacity of regional ecosystem and maintaining regional carbon balance. Although the existing research has achieved remarkable results in regional carbon storage assessment and multi-scenario simulation studies, there are still obvious deficiencies in determining specific carbon storage optimization areas for developed regions and formulating targeted low-carbon development strategies. Taking the economically developed Jiangsu section of the Yangtze River Basin (JS-YRB) as an example, combined with InVEST and PLUS models, the carbon storage and its spatial distribution pattern of the study area in 2030 were predicted under three different scenarios: natural development, cropland protection and ecological protection. The pattern of carbon storage in the study area was optimized by a Bayesian belief network (BBN) with decision optimization ability. The results showed that: (1) From 2000 to 2020, the carbon storage in the study area exhibited a decreasing trend, with a total reduction of 47.98 × 10⁶ t. The primary reason for these decreases was the conversion of cropland and forest land to built-up land. (2) In 2030, under the ecological protection scenario, the carbon storage in the study area would be 390.58 × 10⁶ t, showing an upward trend, while under the other two scenarios, the carbon storage would show a downward trend. (3) Key variables and key state subsets were selected by BBN, and the study area would be divided into four types of optimal zones: ecological protection area, cropland protection area, water conservation area and economic construction area. The findings can provide a reference for the sustainable development of land use within the watershed and contribute to advancing the watershed’s efforts toward achieving the carbon neutrality goals.

Establishing an effective carbon price forecasting model is crucial for promoting the stable development and effective management of carbon trading markets. To enhance forecasting accuracy, this study proposes a hybrid carbon price prediction model based on secondary decomposition and multi-scale forecasting. First, a WOA-XGBoost model is constructed for the initial carbon price prediction. Then, the residuals of the initial prediction are decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the component with the highest fuzzy entropy (IMF1) is further decomposed using Variational Mode Decomposition (VMD). The residual components are then reorganized based on their frequency characteristics. Subsequently, different explanatory variables are introduced to model the high- and low-frequency sequences separately. Finally, the prediction results of each sequence are aggregated to obtain the final composite forecast of carbon prices.The results show that: (1) compared with benchmark models, the proposed hybrid model achieves the best overall forecasting performance, with MAE values of 0.0006 and 0.0013 and R² values of 0.9999 in the Hubei and EU carbon markets, respectively; (2) historical carbon prices are the most influential factor in carbon price forecasting. The Baidu Index contributes most significantly in the Hubei market, while the German DAX index has the greatest impact on the EU carbon market. This model framework provides high-precision quantitative support for carbon allowance pricing, policy evaluation, and cross-market linkage analysis, thereby facilitating the transition of carbon markets toward refined governance and global coordinated emission reduction, and promoting green and sustainable development.

While the environmental implications of economic complexity and clean technology innovations have been individually addressed across various empirical contexts, their joint dynamics in fostering ecological resilience, specifically with the advanced economies, remain analytically unsettled. In this context, by farming the analysis within the Load Capacity Curve (LCC) hypothesis, the study assessed the direct impacts of economic prosperousness, economic complexity, clean technology innovation, financial advancement, and dirty, and clean energy on the ecological resilience. Notably, the objective of the study is to analyze the moderating effect of clean technology innovations in the economic complexity-ecological resilience relationship, considering the Group of Seven (G7) economies over the 1995–2020 period. The findings of the Method of Moments Quantile Regression and Fully Modified Ordinary Least Squares validated the LCC hypothesis. While economic complexity and reliance on dirty energy are associated with ecological degradation, clean energy, financial advancement, and clean innovation show resilience-enhancing effects. Importantly, the positive coefficient of the clean innovation-economic complexity interaction term elucidates that the innovation pattern facilitates a shift toward eco-sustainable economic sophistication. Hence, G7 economies are advised to encourage investments in sophisticated clean technologies like resource-efficient manufacturing processes to counteract the ecological aftermath of complex production.

Background

The government of Indonesia, as a party to the UNFCCC, has committed to reducing its emissions unconditionally by 31% and conditionally up to 43% from the Business as Usual (BAU) level. Land Use Change and Forestry (LUCF) has been targeted as the main sector to meet this commitment, with a great contribution from carbon-rich ecosystems (e.g., peatlands). The sector is expected to reach about 63% of the target. Financially, participation from the private sector to meet the target is crucial. While abundant studies have calculated mitigation costs from the land sector, most of the studies were outdated, and the indirect and transaction costs are rarely taken into consideration. As we perceive such information as key for planning mitigation strategies, we aim to assess the cost required for the implementation of LUCF mitigation with the inclusion of formal transaction costs.

Results

This study uses the Comprehensive Mitigation Assessment Process (COMAP) model, an open spreadsheet model that captures both carbon and economic benefits from mitigation activities. The results showed that mitigation costs for the LUCF sectors ranged from the lowest of USD 10 to almost USD 3,200 per ha, with the most cost-effective options (USD per ton of C) being forest conservation and peatland management activities. To achieve the unconditional NDC target, the total cost required for investment and life cycle costs amounted to USD 11,229 million and USD 34,280 million, respectively, of which 53% of it expected to be provided by the private sector, while the remaining 47% from the state budget.

Conclusions

The study found that mitigation activities by the private require higher life cycle costs due to a large portion of indirect and transaction costs that accounted for 10–43% and 4–19% of the total cost, respectively. Considering the financially significant contribution from the private sector to achieve the NDC target, and to increase the participation of non-party actors to the NDC target, this study proposes more policy instruments made available for cutting these formal transaction costs and to more diversify financing sources.

Urban passenger transportation, as a pivotal element of the transportation system, accounts for over 40% of total carbon emissions from road transport. Consequently, mitigating carbon emissions in this sector is a crucial strategy for attaining carbon peak targets. This study centers on Lanzhou, a representative transportation hub city in China, and develops a dynamic model based on the Passenger Urban Transportation Carbon Emission System (PCES) framework to simulate emissions under three categories of interventions: Demand, Management, and Technology (DMT). The investigation analyzes the temporal trends and underlying mechanisms influencing these emissions. Results reveal that total carbon emissions from passenger transportation in Lanzhou are projected to rise until 2030, with a marked deceleration in growth rate anticipated after 2028. The carbon reduction efficacy among different interventions varies significantly, with fuel vehicle restrictions and management policies demonstrating the greatest effectiveness in conserving energy and reducing emissions. Nevertheless, continuous technological innovation and strategic policy guidance remain indispensable, especially to enhance public transportation usage and reduce overall energy consumption. Furthermore, the integration of multiple strategies accelerates progress toward achieving the ‘carbon peak’ objective within the passenger transportation sector. Simulation outcomes from the combined DMT scenario exhibit superior explanatory power regarding carbon reduction effects within the PCES framework compared to individual measures. Moreover, this research substantiates the utility of the PCES framework in steering the low-carbon development pathway of urban passenger transportation.

Background

The European Union (EU) has promised to achieve net-zero emissions via carbon neutrality by 2050. The aim of this research is to evaluate carbon neutrality efficiencies on forest and agricultural lands in the EU.

Results

The study finds that carbon neutrality efficiency can be computed by a multiplication of carbon emissions efficiency, carbon sink efficiency, and the target of carbon neutrality. We further divide the EU countries into a green group with continuous carbon emission reduction, a gray group with a continuous carbon emission increase, and a mixed group without a continuous carbon emission reduction or increase.

Conclusions

The findings are as follows. (i) Carbon neutrality management is less of a focus than carbon emission management. (ii) There is a trade-off relationship between forest carbon neutrality efficiency and agricultural land efficiency. (iii) Countries in the green group exhibit great heterogeneity in their GDP than those in the gray and mixed groups. (iv) The green group countries exhibit heterogeneous economic structures, and their carbon neutrality performance reflects the overall pattern observed in the EU.

In the context of China’s “dual carbon” goals, whether carbon emission trading, as a typical market-based environmental regulation instrument, can promote corporate green management innovation has become an important topic of academic inquiry. Based on panel data of Chinese listed enterprises from 2008 to 2022, this study investigates the effect of the carbon emission trading policy on green management innovation and explores the underlying transmission mechanisms. The estimated results show that (1) the carbon emission trading policy significantly promotes green management innovation among enterprises, and this effect remains robust after a series of sensitivity tests; (2) mechanistically, the policy facilitates green management innovation primarily by reducing managerial myopic behavior and increasing access to green credit; and (3) the policy exerts a stronger effect on enterprises with environmentally experienced executives, higher managerial capability, higher carbon intensity, greater industry competition, and larger scale. The findings provide empirical evidence that enhancing green innovation can support the green and low-carbon transition of enterprises in developing countries.