Carbon Balance and Management最新文献

Background

Estimates of aboveground woody plant biomass in hyper-arid ecosystems have predominantly relied on allometric equations developed in more mesic habitats. However, these equations do not account for local variations in plant morphology, necessitating the development of equations for the hyper-arid context. Here, we present species- and growth-form-specific allometric equations for 11 woody plant species in AlUla County, Kingdom of Saudi Arabia (KSA), based on sample sizes ranging from 8 to 50 individuals per species.

Results

Across five nature reserves in AlUla County, individuals of each selected plant species, spanning a range of size classes, were measured for height and crown area. For tree species with suitable structures (i.e. Moringa peregrina and Vachellia gerrardii), basal diameter was also recorded. All sampled plants were then destructively harvested to determine aboveground biomass. For all six shrub species, the best-fitting allometric equations included crown area and height as predictors of aboveground biomass, whereas all five tree species’ equations included height (and other predictors, varying by species). The best-fitting general multi-species equations included crown area and height as predictors of aboveground biomass for both shrub and tree growth forms.

Conclusions

The predictors in the best-fitting equations likely reflect the branched, lateral growth forms characteristic of plants in hyper-arid ecosystems, and are expected to improve the accuracy of biomass estimation compared with equations developed in mesic environments. These allometric equations provide a novel foundation for the quantitative monitoring of aboveground plant biomass and carbon stocks in the KSA and hyper-arid regions further afield.

Accelerating urban low-carbon transition is vital for high-quality economic growth and sustainable development. The energy industry, a key driver of socioeconomic progress, is the main source of carbon emissions and is crucial for decarbonization. Optimizing its structure is essential for urban low-carbon transformation. Based on the panel data of 284 prefecture-level cities in China from 2003 to 2022, this study decomposes the optimization of the energy industry structure into basic structure optimization and spatial structure optimization, uses green total factor productivity (GTFP) to measure the level of urban low-carbon transformation, and uses the super-efficiency SBM model to calculate it, deeply explores whether and how the optimization of the energy industry structure affects the low-carbon transformation of cities and analyzes the mechanism effect of the level of green technology innovation in it. The results show that both the optimization of the energy industry basic structure and the optimization of the energy industry spatial structure can promote the low-carbon transformation of the city, and the results are still valid after the endogenous test and robustness test. The level of urban green innovation will play a positive moderating effect on the optimization of the energy industry structure on its low-carbon transformation and development. The relationship between energy industry structure optimization and urban low-carbon transformation is obviously heterogeneous in different regions, not only in different geographical locations, but also in different levels of economic development.

The global endeavor to combat climate change has elevated environmental sustainability to a critical priority, creating an urgent need for effective strategies to achieve it. This study examines the relationships between green technology innovation, tourism development, financial development, and economic growth on China’s ecological footprint from 1995 to 2023. Using the Quantile Autoregressive Distributed Lag (QARDL) approach, this study analyzes asymmetric effects across different quantiles. The results show that green technology innovation reduces the ecological footprint at higher quantiles (β = −0.096 at the 95th), while tourism development increases environmental degradation, with coefficients ranging from − 0.428 at the 30th to − 0.262 at the 95th quantile. Financial development promotes sustainability at the middle quantiles, although effectiveness diminishes at higher levels. Economic growth worsens the ecological footprint (β = 0.652 at the 80th percentile). Short-run estimates show that tourism development has negative impacts, whereas green technology innovation reduces the footprint at higher quantiles. Moran’s I indices indicate spatial dependence in tourism (0.85 in 2005) and financial development (0.78 in 2002), with green technology showing a weaker clustering. These findings necessitate differentiated provincial policies on eco-tourism, green technology adoption, and sustainable funding to reduce China’s ecological footprint while balancing growth.

The maritime transportation and fisheries sectors play a crucial role in national food security and economic stability; however, they face significant sustainability challenges due to climate change and carbon emissions. This study investigates mitigation scenarios for achieving carbon neutrality in the sector by 2050, addressing both energy demand and emissions mitigation in accordance with the IMO 2023 decarbonization targets. Quantitative modeling using the Low Emissions Analysis Platform system, combined with multiple linear regression analysis, is utilized to simulate long-term energy demand, fuel substitution, and emissions across fisheries and navigation sub-sectors. The analysis compares the baseline scenario of continued fossil fuel reliance with an alternative decarbonization scenario. Under the baseline scenario, energy demand and emissions rise sharply, while the alternative scenario projects a 71.1% reduction in fisheries-related emissions and a 76.4% reduction in the navigation sector by 2050. Fossil fuel dependency declines from full reliance in 2025 to 5% in 2050, replaced by a diversified mix of renewable fuels. Total energy demand stabilizes at 0.723 TWh under the decarbonization pathway compared to 0.980 TWh under baseline conditions. Projected hydrogen adoption grows from 10% in 2030 to 30% in 2050, while biodiesel follows a comparable growth curve. Anticipated mitigation of 1.473 MtCO₂eq underscores the sector’s potential to meet national climate targets when underpinned by regulatory support mechanisms and targeted investments. This study underscores the potential for Albania’s maritime sector to lead decarbonization efforts in the Mediterranean region through an integrated strategy combining policy reform, technology adoption, and regional cooperation.

Background

Forest carbon sink is a natural solution to reduce carbon emissions and balance the carbon cycle, and forest carbon sink loan is an important financial innovation to support cleaner production, but the project promotion faces many challenges. This paper investigates the Anji’s bamboo carbon sink loan project in China and reveals the influencing factors of farmers’ participation.

Results

Farmers are driven by interests and have a high degree of participation in the bamboo forest carbon sink loan program, but they do not know enough about the internal mechanism. Based on the neural network and FP-Growth algorithm association rule analysis, it was found that the willingness of farmers to participate in the project was mainly affected by age, education level, and source of income, and the concentration of the population was characterized by older age and lower education level. Using fuzzy set qualitative comparative analysis, the respondents can be divided into risk-averse groups, active participation groups, experience-dependent groups, and cost-sensitive groups.

Conclusions

Differentiated measures should be implemented for different groups to promote the implementation of carbon sink projects.

To examine the leverage effects of carbon pricing on power sector decarbonization and its underlying mechanisms, in this study, a dynamic emission abatement demand model is developed that incorporates lagged effects, innovatively integrated with convergent cross-mapping (CCM) causality analysis. Leveraging daily transaction data from China’s national carbon market (January 2022–January 2024) and firm-level operational data from thermal power enterprises, we systematically unravel the nonlinear incentive mechanisms of carbon pricing. The key findings include the following. First, carbon prices exert significant amplification effects through cost transmission pathways, with an average short-term elasticity coefficient of 1.78 during trading phases, indicating that a 1% price increase drives a 1.78% marginal emission reduction. Second, CCM causality tests demonstrate that historical cumulative emissions exert threefold stronger causal influence (0.63) on market trading volumes compared to incremental emissions (0.21), validating the consensus-driven emission control under China’s free quota allocation regime and the synergistic efficacy of cap-and-trade mechanisms. Third, while doubling discount factors and carbon asset conversion efficiency yields proportional growth in abatement demand (<100%), a 20% improvement in market liquidity amplifies demand by up to 100%, highlighting liquidity’s critical role in enhancing price signalling efficacy during later trading stages. Our findings suggest that carbon markets can effectively incentivize power sector decarbonization, yet sustained impacts require market designs that balance liquidity optimization, risk mitigation, and dynamic quota allocation to deepen market maturity. This research contributes micro-level empirical evidence and methodological innovations for enhancing carbon pricing efficacy, offering actionable insights for policymakers to refine market mechanisms and accelerate low-carbon transitions.

Amid the deepening implementation of the "dual carbon" strategy, elucidating the multidimensional dynamics of industry-university-research (IUR) collaborative green innovation on regional carbon emissions holds critical significance for reconciling environmental governance with economic development. Leveraging panel data from 30 Chinese provinces (2010–2022), this study employs parametric and non-parametric approaches to decode the nonlinear impact of IUR collaborative green innovation on carbon emissions. Through moderated mediation models and spatial lag analysis, it systematically reveals operational mechanisms. Key findings include: (1) An inverted U-shaped relationship emerges-initial collaboration phases may elevate emissions, but sustained efforts progressively manifest emission reduction effects. (2) Technological substitution drives low-carbon transitions in polluting industries. While restructuring triggers transient carbon pulse peaks from cost surges, long-term trajectories follow inverted U-shaped patterns moderated by industrial composition and structural upgrading. (3) Initial U-shaped suppression effects stem from resource misallocation and adaptation costs, yet enhanced technological absorptive capacity elevates green total factor productivity (GTFP), enabling a 9.57% emission reduction through industrial transformation. (4) Spatiotemporal interactions evolve from short-term U-shaped spatial spillovers to long-term inverted U-shaped synergies, necessitating optimized policy coordination for dynamic emission reduction dividends. (5) Regional heterogeneity persists-eastern China demonstrates stable impacts through industrial maturity, contrasting with volatile central/western regions constrained by fragmented innovation ecosystems. This research advances understanding of collaborative innovation’s nonlinear carbon governance effects, offering actionable insights for regionalized decarbonization strategies and cross-regional innovation alliances.

Background

Biochar effects on soil organic matter stability in permafrost regions remains poorly understood. To address this knowledge gap, two-cycle incubation experiments using representative forest and peatland soils were conducted from Daxing’anling permafrost region. Soils with corn straw-derived biochar (pyrolyzed at 450 °C, 2 h) were amended at 8% w/w of dry soil weight and systematically measured soil organic carbon (SOC), total nitrogen (TN), dissolved organic carbon (DOC), carbon fractions, microbial community, carbon emission, and CO₂ isotope content.

Results

The research indicated that biochar amendment improved physicochemical properties in both soil types. Electrical conductivity increased by 166.62% (forest) and 223.79% (peatland), while SOC increased by 60.57% (forest) and 5.64% (peatland). Mineral-associated organic carbon increased significantly (particularly in forest soils), which also exhibited increases in TN (32.15% at 180 days) and DOC (197.50% at 90 days). Biochar addition reduced the diversity and richness of bacterial communities in forest soils, but had no significant effect on peatlands.

Conclusion

Biochar promoted soil aggregate formation, improved soil carbon sequestration capacity, and reduced CO₂ emissions by 19.37% (forest) and 9.70% (peatland). These findings confirmed the dual functionality of biochar in increasing soil carbon storage and reducing carbon emissions. The study provides valuable insights for enhancing carbon management strategies in vulnerable permafrost ecosystems, emphasizing the potential of biochar in soil management.

Under China’s “dual carbon” goals, local officials bear primary responsibility for reducing carbon emissions within their jurisdictions. This paper investigates whether mayors’ professional backgrounds are associated with better performance in achieving emission reduction outcomes. Using Panel data from prefecture-level cities between 2005 and 2016, we find that mayors with engineering backgrounds significantly reduce carbon emission intensity. This effect is pronounced in in megacities, industrial hubs, eastern regions, and cities with stronger economic foundations. Mechanism analysis reveals: engineering-trained mayors possess stronger technical expertise and systematic, pragmatic thinking, enabling them to foster greater local green innovation—both in quantity and quality—and to adopt high-intensity low-carbon policies, particularly market-based instruments. These findings highlight that appropriately appointing mayors with engineering expertise represents a distinctive and effective policy instrument for achieving China’s dual-carbon goals. This also underscores the importance of incorporating technical expertise into cadre selection and evaluation systems.

Background

Water and land resources are important for maintaining the sustainable development of society. However, with the utilization of water and land resources, a large amount of carbon emissions will be generated. Therefore, studying carbon emissions under the water-land-carbon connection is of great significance for achieving “dual carbon goals”. This paper first calculated the land use carbon emissions and the total carbon emissions in Shandong Province. Secondly, the carbon emission economic contribution coefficient (EC), carbon water coefficient (CWC), carbon emission intensity (CI), and coefficient of variation (CV) were constructed. The center of gravity-standard deviation ellipse was used to determine the spatio-temporal distribution characteristics of carbon emissions. Finally, the Kaya-LMDI model was used to investigate the factors that influence carbon emissions.

Results

(1) The land use and total carbon emissions of the Provincial Capital Economic Circle (PEC) are more than those of the Jiaodong Economic Circle (JEC) and those of Lunan Economic Circle (LEC). For EC, PEC is greater than LEC is greater than JEC. For CWC, JEC is greater than PEC is greater than LEC. For CI, LEC is greater than PEC is greater than JEC. (2) The CV of carbon emissions in the province is at a low level, indicating a small fluctuation in carbon emissions. The spatial–temporal distribution of the land use carbon emissions is generally from northeast to southwest, and the center of gravity migration track is from northwest to northeast to southwest. The distribution of the total carbon emissions changes from northeast-southwest to southeast-northwest, and the shifting track is east-southwest. (3) Carbon emission efficiency effect, land economy effect, and population effect promote carbon emission; water use intensity effect and per capita land use effect inhibit carbon emission.

Conclusions

PEC gives priority to promoting the adjustment of industrial structure and the development of renewable energy; JEC strengthens the application of water-saving and recycling technologies; LEC optimizes land efficiency, develops low-carbon agriculture and strictly controls high energy-consuming projects. This result provides a new perspective and practical basis for urban collaborative carbon reduction.