Evaluating the degree of coordination among regional carbon emission systems is key to achieving an earlier carbon peak and carbon neutrality. However, quantifying the co-evolution of carbon emissions among regions is challenging. Therefore, we propose a data-driven method for evaluating the synergetic development of the regional carbon emission composite system. First, the proposed method employs relevant data to calculate the carbon emissions and carbon emission intensity of each subsystem within the region to describe the temporal trends. The inverse entropy weight method is then used to assign weight to each order parameter of the subsystem for data processing. Then, we perform synergetic development assessment of the composite system to measure the order degree of each subsystem, the degree of synergy among subsystems, and the overall synergetic degree of the temporal evolution of carbon emissions between regions. Finally, the evaluation results can be used to suggest measures for the regional coordinated reduction of carbon emissions. In this study, we used data from the Yangtze River Delta (YRD) region from 2010 to 2019 to demonstrate the feasibility and effectiveness of the method. The results show that there is still a long way to go to reduce carbon emissions in the Yangtze River Delta region. Economic development still relies heavily on fossil energy consumption, and the regional carbon emission reduction synergy is not high. This study provides theoretical and methodological support for regional carbon emission reduction. Moreover, the proposed method can be applied to other regions to explore low-carbon and sustainable development options.

The receding globalization has reshaped the logistics industry, while the additional pressure of the COVID-19 pandemic has posed new difficulties and challenges as has the pressure towards sustainable development. Achieving the synergistic development of economic, social, and environmental benefits in the logistics industry is essential to achieving its high-quality development. Therefore, we propose a data-driven calculation, evaluation, and enhancement method for the synergistic development of the composite system of economic, social, and environmental benefits (ESE-B) of the logistics industry. Based on relevant data, the logistics industry ESE-B composite system sequential parametric index system is then constructed. The Z-score is applied to standardize the original index data without dimension, and a collaborative degree model of logistics industry ESE-B composite system is constructed to estimate the coordinated development among the subsystems of the logistics industry’s ESE-B system. The method is then applied to the development of the logistics industry in Anhui Province, China from 2011 to 2020. The results provide policy recommendations for the coordinated development of the logistics industry. This study provides theoretical and methodological support for the sustainable development aspects of the logistics industry.

Rapid economic development has exacerbated environmental degradation, particularly because of carbon dioxide emissions. To address these issues, China has proposed economic transformation from high-speed to high-quality development to achieve carbon peak and neutrality. Regional collaborative carbon emission reduction is critical for sustainability. Therefore, measuring regional carbon emission reduction capacity and the collaborative development level is imperative. This study employed provincial- and city-level data (2014–2023) from the Yangtze River Delta to assess regional collaborative carbon emission reduction capacity. Evaluation model of carbon emission reduction capacity was constructed based on five perspectives: economic development, carbon emission, carbon transfer, carbon sink, and industrial development. The entropy weighting method was employed to assign index weights, which was then integrated with a composite system synergy degree model. The subsystem order parameters and the composite system’s order degree were utilized to assess carbon emission reduction and collaborative trends. Results revealed that (1) overall carbon emission reduction capacity in the Yangtze River Delta constantly improved; (2) provincial economic development strengthened while carbon emissions declined; (3) carbon transfer fluctuations decreased; (4) technology and carbon sinks improved; (5) industrial development fluctuated or declined; and (6) interregional carbon emission reduction cooperation remained stable and improved. This research offers a theoretical and scientific reference for formulating low-carbon development strategies in similar regions.

Environmental risks, including climate variability, resource scarcity, and policy changes, have increased uncertainty and price volatility across global commodity markets. Although a growing body of literature has examined commodity price dynamics, spillovers, and cross-market dependence, relatively fewer studies integrate cross-commodity dependence, downside tail risk, and regime-sensitive portfolio construction within a unified environmental risk management framework. This study evaluates whether cross-commodity futures portfolios can serve as tools for resource-price risk management under environmental and market uncertainty. Using a comprehensive dataset covering energy, agricultural, metal, and livestock futures, the analysis combines conditional value-at-risk (CVaR), rolling correlation analysis, principal component analysis, and regime-based portfolio evaluation. Hierarchical risk parity (HRP) and CVaR-minimizing portfolios are evaluated under both low-risk and high-risk market regimes. The results show that risk-oriented portfolio strategies substantially reduce volatility, downside tail risk, and maximum drawdown relative to an equal-weighted benchmark. The dependence analysis further indicates that energy markets constitute the dominant common factor driving cross-commodity risk transmission. Regime analysis and state-space estimation reveal that systemic commodity risk evolves dynamically and becomes more pronounced during periods of elevated market stress. Overall, the findings suggest that commodity-based financial indicators can complement traditional environmental monitoring by providing market-based information on evolving systemic risk under environmental and market uncertainty.

Shared bicycle systems are widely promoted as sustainable urban mobility solutions due to their potential for resource sharing and emission reduction. However, assessments of their sustainability performance often rely on aggregate usage indicators, providing limited insight into how shared mobility assets are actually utilized across time and space. From a circular economy perspective, uneven utilization patterns may undermine resource efficiency and long-term infrastructure sustainability. This study examines shared bicycle systems as circular urban infrastructure by analyzing temporal and spatial patterns of resource utilization during a public holiday period. Using large-scale shared bicycle order data from Shenzhen during the Labor Day holiday, the study investigates how demand is distributed within daily cycles and across urban space, with particular attention to concentration and inequality in usage. The results reveal pronounced temporal and spatial concentration of shared bicycle trips. A substantial proportion of trips is concentrated within a limited number of peak hours, while spatial usage remains highly clustered in specific urban zones. Importantly, these concentration patterns persist even during peak demand periods, indicating limited temporal and spatial elasticity of the system under stress conditions. These findings suggest that the sustainability of shared bicycle systems depends not only on overall demand levels, but also on the structure of resource utilization. By highlighting distributional inefficiencies in time and space, this study contributes to ecological economics and management research on circular urban infrastructure and offers practical insights for the governance and operation of shared mobility systems.