Carbon Balance and Management最新文献

The multi-energy complementary power system achieves comprehensive and synergistic utilization of diverse energy sources, generating large-scale and distributed operational data. This introduces challenges in leveraging operational data for accurate and efficient carbon emission prediction. To effectively process the large-scale distributed operational data of power systems, identify key influencing factors, and achieve high-precision carbon emission prediction, this study investigates a carbon emission prediction method for multi-energy complementary power systems based on a multiple linear regression model. The structure of the multi-energy complementary power system is analyzed, and its carbon emission intensity is calculated. Based on the analysis results, preliminary selection of carbon emission influencing factors is conducted. A multiple linear regression model is constructed with the selected factors as independent variables and carbon emissions as the dependent variable. By performing significance tests on each independent variable, key influencing factors are identified, yielding an optimized multiple linear regression model. The model is integrated into the MapReduce parallel framework to expand computational scalability, enabling parallel processing of large-scale distributed power system data while ensuring prediction efficiency. The results demonstrate that the selected factor variables are reasonable, and the constructed prediction model exhibits a high goodness-of-fit. The prediction error ranges between 0.00516% and 0.00818%, confirming high accuracy and efficiency. The prediction results indicate that the experimental multi-energy complementary energy center's carbon emissions increase annually from 2025 to 2031 and gradually decline from 2031 to 2034. These findings provide a scientific basis for formulating carbon emission reduction policies in multi-energy complementary power systems.

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.

As a core metric for climate policy, the scientific estimation of carbon social costs is crucial for formulating mitigation strategies. However, traditional integrated assessment models predominantly focus on the global aggregate, failing to adequately account for regional heterogeneity, sectoral characteristics, and strategic interactions between regions. They also lack systematic integration of ESG principles. To address this, this paper examines regional and sectoral carbon social costs driven by ESG development. Through cooperative and non-cooperative games, we improve the integrated economic-environmental-climate development model, take the eight economic regions in China as an example, get the carbon social cost of each economic region and typical important industries, and obtain the key parameters and the evolution law of carbon social cost. The model categorizes the carbon emissions after the implementation of emission reduction policies under the ESG perspective into direct and indirect emissions. It studies the economic impacts of the two types of emissions before and after the implementation of emission reduction policies, and conducts research on the top four typical important industries (industry, construction, transportation, and power) that rank among the top four global CO₂ emitters, to obtain the analytical solution of the social cost of carbon in the region and the typical important industries. In addition, this paper numerically simulates the social cost of carbon for the four industries under the baseline scenario, cooperative game scenario, non-cooperative game scenario, and temperature limitation scenario. The study shows that the social cost of carbon in the northern, southern and eastern coastal economic regions is higher than that in other economic regions, the social cost of carbon in the industrial and electric power industries in each economic region is higher than that in the building and transportation industries, and the more stringent the temperature limit is, the higher the social cost of carbon is in the economic regions.

As a far-reaching initiative in China's air pollution control and energy transition efforts, the clean heating policy has sparked considerable debate in both academia and practice regarding its effectiveness in reducing carbon emissions. This study uses panel data from 15 prefecture-level cities in northern China from 2013 to 2023 and constructs a multi-period difference-in-differences model to empirically examine the impact of the clean heating policy on regional carbon emissions. The results are summarized as follows: (1) The policy effectively promotes the reduction of regional unit GDP and per capita carbon emission intensity in Northern China, but it has no evident effect on regional total carbon emissions. (2) The policy can exert the multiplier effect of the central government funds and structural effect to facilitate regional low-carbon transformation, but no significant Porter effect has been observed. (3) The carbon reduction effects exhibit significant regional heterogeneity. The policy has a more significant effect on carbon emissions of nonprovincial capital cities, coal-resource cities, and regions without coal power output, but it may significantly increase emissions in coal power-exporting regions. The clean heating policy should continue to be vigorously implemented, but its implementation strategy should be optimized by strengthening the transmission mechanism and addressing regional differences.

This study investigates the spatial variability of forest fire intensity, burn indices, ecosystem productivity, and Greenhouse Gas (GHG) emissions in Pakistan from 2001 to 2023. Using satellite-derived burn indices such as SAVI, LST, NMDI, LSWI, NBR, and MSAVI2, the study examines the relationship between forest fires and net primary productivity (NPP) across diverse ecological regions. The analysis reveals that northern Pakistan, particularly Khyber Pakhtunkhwa and Gilgit-Baltistan, experiences high fire intensity, resulting in significant reductions in NPP and increased emissions of COx, NOx, and CH₄. Central and southern Pakistan, including the arid regions of Balochistan and Sindh, exhibit lower fire intensity but remain vulnerable due to climate-driven dry conditions. The study also applies the ΔNPP/ΔBurn approach to evaluate how changes in burn indices correspond to shifts in NPP, revealing that small increases in fire intensity can lead to substantial ecosystem productivity loss. Additionally, a comparative analysis of Random Forest (RF) and XGBoost machine learning models for fire prediction found RF to be the more accurate model, achieving 88.0% accuracy and a 93.8% AUC score. These findings underscore the importance of developing region-specific fire management strategies to mitigate the ecological and environmental impacts of wildfires. The study highlights the critical need for improved fire prediction, early warning systems, and long-term monitoring of post-fire ecosystem recovery. By drawing comparisons with global research, this study contributes to understanding the broader implications of forest fires on carbon dynamics and ecosystem productivity, providing valuable insights for future fire management policies in Pakistan.

This study is one of the few contextual and integrative empirical studies examining how artificial intelligence marketing efforts (AI MEs) can shape consumers' green purchasing behavior (GPB), particularly among Generation Z consumers in developing countries, and thus contribute to green consumption and brand element relationships. This study investigates the direct and mediating effects of both AI MEs (information, customisation, interaction, and accessibility) and brand elements (brand preference, brand experience, and brand trust) on consumers' GPBs based on the SOR model. An analysis based on surveys (n = 609; SEM-ANN) revealed that AI MEs significantly affected brand elements and GPB, and that brand experience and brand preference were significant mediators in the relationship between AI MEs and GPB. The study also found a significant relationship between brand elements and GPB in the present study. The ANN analysis showed that the most important variables in explaining GPB were brand preference, AI MEs, and brand experience. By integrating AI marketing and brand elements into the conceptualisation of GPB, this study contextually enriches and integrates the limited body of knowledge on sustainable consumer behaviour. The findings offer new theoretical insights and practical guidance for policymakers and businesses aiming to leverage AI to promote environmentally responsible consumption.

Background: The transportation sector, as a significant contributor to global CO₂ emissions, demands urgent attention to align its decarbonization with national carbon neutrality agendas. Existing research disproportionately focuses on quantifying TCE (transportation CO₂ emissions) and mapping their spatio-temporal distributions. However, the evolutionary trajectories and sequential peaking dynamics of TCE across different national contexts remain unclear. To address this question, this study was conducted.

Results: A global comparative analysis of 115 countries was conducted, establishing a four-stage TCE development typology through three metrics: TCE intensity (A), per capita TCE (B), and total TCE (C). The analysis revealed a universal A → B → C peaking sequence, with Stage II (A to B transition) exhibiting a significantly prolonged duration (mean = 8.37 years) compared to Stage III (B to C transition; mean = 2.12 years). Developed economies predominantly occupy Stage IV, while developing countries cluster in Stage II and Stage III. Regionally, North American countries demonstrated extended durations in both stages, exceeding global averages. Regression analysis indicated that socioeconomic indicators have limited explanatory power in predicting stage durations, underscoring the individualized nature of TCE progression across nations.

Conclusions: This study contributes by revealing the unified and diverse peak patterns of three core TCE indicators at national levels, while addressing a critical gap in global emission reduction strategies through cross-economy analysis. The findings confirm a predictable evolution in TCE across most nations but highlight significant variations between developed and developing economies. The prolonged duration of Stage II compared to Stage III suggests a more challenging transition phase for many countries. Moreover, the limited influence of standard socioeconomic metrics on stage durations emphasizes the need for nuanced, country-specific approaches to emissions transitions. The study proposes targeted TCE reduction measures differentiated by development stage and transportation sub-sector, providing scientific guidance for policy formulations.

The rapid increase in municipal solid waste (MSW) generation across urban centers in Pakistan, combined with insufficient waste management infrastructure, presents a significant environmental and public health challenge. This study assesses methane emissions and leachate generation from major MSW dumpsites in Rawalpindi and Lahore, two of Punjab province's largest cities. Emissions were estimated and projected over a 50-year active timespan using the U.S. EPA LandGEM model following IPCC 2006 guidelines. Cumulative emissions from Lahore's solid waste disposal (SWD) systems were calculated at approximately 133,446 Gg, equivalent to 108 Mt CO₂-eq, with contributions comprising 26% methane, 73% carbon dioxide (CO₂), and 0.2% non-methane organic compounds (NMOCs). In contrast, Rawalpindi's SWD systems generated 958 Gg (or 7.8 Mt CO₂-eq) over their operational life, exhibiting a similar emissions profile. Two unmanaged Lahore sites-LD2 (1643 Gg CH₄) and MB1 (1383.9 Gg CH₄)-emerged as the most significant methane emitters across both cities. These results underscore the urgent need for targeted waste management strategies, particularly the deployment of methane capture technologies and effective leachate treatment systems. The study highlights the substantial greenhouse gas emissions and groundwater contamination risks posed by unmanaged landfills. To mitigate these impacts and align with national climate goals, the adoption of site-specific policies and sustainable waste-to-energy solutions is imperative.

The world economies have environmental sustainability as one of their main concerns. Although numerous studies have been conducted to determine the factors contributing to ecological problems, little has been done to determine the consumption-based CO₂ emission as a sign. The current research focuses on analyzing the factors that define consumption-based CO₂ emissions based on financial growth, innovation of green technology, urbanization and trade openness of Asian nations between 1991 and 2020. We employed CS-ARDL to address the cross-section dependency and heterogeneous slopes in a panel estimation. The outcomes indicate that the overall trend of financial development is that of the rise of carbon emissions, but the application of green technology considerably decreases it. Furthermore, the relationship between the financial development and green innovation assists in neutralizing some of the environmental effects of financial growth. Urbanization and openness to trade, on the other hand, have minimal effect on the CO₂ emissions. The Granger causality tests also point towards the interrelationships between financial structure, industrial activity, green technology and emissions dynamics. The general conclusions imply that the government comes up with effective financial policies that offer financial incentives in encouraging green innovation to mitigate carbon emissions.