Search Results (6,312)

Industrial digitalization reshapes production processes and can potentially improve carbon productivity by optimizing factor allocation and energy efficiency. Using panel data for 30 Chinese provinces from 2012 to 2022, this study constructs a comprehensive industrial digitalization index with four dimensions and 13 indicators using the entropy method and examines its impact on carbon productivity (GDP per unit of CO₂ emissions). We employ the Dagum Gini coefficient and kernel density estimation to describe regional disparities and their evolution, a dynamic panel threshold model to test the nonlinear role of industrial transformation and upgrading, and a spatial Durbin model to identify spatial spillover effects. The results indicate that industrial digitalization has risen nationwide but remains uneven; industrial digitalization significantly enhances carbon productivity, with stronger effects in the eastern and western regions and in plain areas; the effect exhibits a double-threshold pattern with respect to industrial transformation and upgrading, implying a U-shaped relationship; and industrial digitalization generates positive spatial spillovers. These findings suggest that policy should coordinate digital infrastructure investment with industrial upgrading and regional collaboration to accelerate low-carbon, high-efficiency growth. Full article

Against the backdrop of accelerating global carbon neutrality and the full implementation of China’s “Dual Carbon” strategy, the mining industry, as an energy-intensive sector that guarantees resource supply, plays a critical supporting role in the green transformation of the industry and achieving national carbon emission reduction targets. Based on panel data from 29 provinces in China from 2000 to 2021, this study combines the Tapio decoupling index and the LMDI decomposition method to systematically characterize the evolution of carbon emissions in China’s mining industry, to accurately identify the decoupling state between carbon emissions and economic growth, and to reveal the core driving mechanism, presenting quantifiable and interpretable empirical and technical results. The results show that carbon emissions and raw ore output in China’s mining industry generally followed an evolutionary trend of “first rising, then peaking, and continuously declining”. Carbon emissions peaked in 2013 and decreased steadily afterward, reflecting remarkable achievements in green development. The decoupling relationship has shifted from weak decoupling to stable strong decoupling in 2019 and has been maintained in this state ever since, indicating that the mining industry has entered a high-quality development stage featuring coordinated economic growth and carbon emission reductions. The decomposition results confirm that the output expansion effect is the main driver of the increase in carbon emissions, while the reduction in energy intensity, optimization of the energy structure, and improvement in output efficiency constitute the key forces driving the reduction in carbon emissions, with technological progress, industrial upgrading, and clean energy substitution as the core pathways. In summary, this study empirically verifies the feasibility and effectiveness of low-carbon transformation in China’s mining industry. The realization of a stable strong decoupling state shows that this paradigm can be replicated in the green development of other energy-intensive industries. In the future, precise policy incentives, energy structure upgrades, energy efficiency technological innovation, and standardized construction of green mines can further consolidate the decoupling effects and further encourage the comprehensive transition towards a low-carbon mining industry. The findings of this study can provide a solid theoretical basis and empirical support for the formulation of carbon emission reduction policies and the design of green development pathways in China’s mining industry, with important theoretical and practical value for ensuring national resource security and facilitating the realization of the “Dual Carbon” goals. Full article

Landfill leachate is considered one of the most recalcitrant wastewaters due to its high organic strength, elevated ammonia concentrations, and complex chemical composition. This study evaluates integrated technologies for treating highly loaded landfill leachate from the Wadi Al-Asla landfill, Jeddah Saudi Arabia, using GPS-X^TM modeling combined with regulatory compliance and techno-economic assessment (TEA). The characterized mature leachate exhibited extremely high average concentrations of COD (17,050 mg L⁻¹), BOD₅ (10,058 mg L⁻¹), ammonia-N (989 mg L⁻¹), and total nitrogen (1223 mg L⁻¹), indicating severe pollution levels requiring integrated treatment technologies. Five (5) different scenarios involving integrated biological, physicochemical, and membrane-based processes were modelled, simulated and evaluated against local discharge standards complaince. Conventional and municipality-proposed upgrade configurations achieved ~80–83% COD removal, producing effluent COD > 2900 mg L⁻¹ and 1790–1801 mg L⁻¹ BOD₅, indicating persistent non-compliance for organic pollutants. Nitrogen removal improved substantially (93.7–95.7% ammonia-N and 91–93% total nitrogen removal), yet residual ammonia-N (44–63 mg L⁻¹) and total nitrogen (92–108 mg L⁻¹) remained above regulatory limits. Advanced hybrid systems achieved complete TSS removal and strong phosphorus control (TP ≤ 0.42 mg L⁻¹), while three(3) compartmental aerobic–anoxic membrane bioreactor coupled with reverse osmosis (MBR + RO) achieved near-complete nitrogen removal and reduced 90% COD removal. The lifecyle economic assessment indicated OPEX ranging from USD 1.1 to 5.6 m⁻³ of treated leachate with the aerobic–anoxic MBR + RO configuration yieding footprint advantage, lower CAPEX and moderate OPEX By combining process modeling, regulatory compliance evaluation, and economic assessment, this study provides a practical screening framework for selecting sustainable treatment strategies for high-strength landfill leachate and wastewater matices. Full article

China’s dual-carbon targets necessitate a transition toward a greener, safer, and more efficient energy system; however, substantial disparities persist across provinces. This study evaluates high-quality energy development across 30 Chinese provinces (2011–2022) under the dual-carbon agenda and identifies differentiated transition pathways. Using a PCA-TOPSIS framework with regional pattern classification, we find an “east-high, west-low, central-dip” spatial structure and a nationwide improvement trend over time. Beijing and Guangdong remain persistent leaders, whereas the central region is the primary weak link. Green energy and energy innovation are the strongest contributors to provincial performance, highlighting the importance of clean supply and technological capability. Policy implications emphasize differentiated approaches: strengthen innovation leadership in the east, accelerate structural upgrading and clean substitution in central and resource-dependent provinces, and improve infrastructure and market integration to unlock renewable advantages in the west. Full article

As a production factor, data plays an increasingly important role in sustainable development. Using panel data from 31 Chinese provinces (2011–2023) and employing fixed-effects, panel threshold, and spatial Durbin models, this study examines the impact of data element agglomeration on inclusive green development. The results reveal four main findings. First, data element agglomeration significantly improves inclusive green development, though this positive effect stems primarily from economic growth and social inclusion rather than direct environmental gains. Second, industrial structure upgrading and green technology innovation function as underlying mechanisms, but exhibit suppression effects—their indirect contributions are negative, reflecting short-term adjustment costs and institutional frictions. Third, fiscal support intensity exhibits a nonlinear moderating effect with diminishing returns. Fourth, the effect is more pronounced in coastal provinces, regions with stringent environmental regulation, and the pre-2020 period, and generates significant spatial spillovers to neighboring regions. These findings highlight the conditional nature of data-driven green development and offer insights for designing context-sensitive data policies. Full article

The recycling of spent lithium iron phosphate (LiFePO₄, LFP) batteries is receiving increasing attention as electric-vehicle deployment accelerates worldwide. Pyrometallurgical reduction offers a viable route for large-scale recovery of iron-rich products from spent LFP batteries; however, the resulting Fe-based alloys often retain residual copper (Cu), which deteriorates alloy quality and constrains downstream utilization and refining. In this study, a sulfidation–slag refining process was developed to selectively remove Cu from an Fe–P–Cu alloy produced by dry reduction of spent LFP batteries. FeS was employed as a sulfidizing agent to promote preferential conversion of Cu into sulfide phases, while fayalite (Fe₂SiO₄) slag was introduced to enhance phase separation between metallic and sulfide/slag phases. Thermodynamic calculations coupled with high-temperature experiments were conducted at 1400–1600 °C under various Cu:FeS ratios to identify operating conditions that maximize Cu removal while minimizing Fe loss. The results indicate that Cu is selectively transferred from the metallic phase to Cu–Fe–S sulfide phases, whereas Fe remains predominantly in the metal phase. Under the optimal condition (1400 °C, Cu:FeS = 2:1), the refined metal reached an Fe content of 90.80 wt.%, achieving an Fe recovery of 87.42% and a Cu removal efficiency of 81.13%. The proposed approach provides a practical stepwise refining strategy for upgrading Fe-rich secondary resources recovered from spent LFP batteries and facilitates subsequent impurity-control processes. Full article

Green finance has emerged as a crucial instrument for driving the macroeconomic transition toward a low-carbon economy, yet its specific transmission mechanisms warrant deeper empirical scrutiny. Leveraging China’s Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment, this scientific study employs a staggered difference-in-differences (DID) framework using provincial panel data from 2009 to 2023. To overcome the limitations of unidimensional metrics, we developed a comprehensive Industrial Structure Upgrading Index (ISUI) that integrates structural rationalization, advancement, and greening. The empirical findings reveal that the green finance pilot policy exerts a significant and positive impact on the ISUI. This core result remains robust under a series of rigorous checks, including the Callaway and Sant’Anna (CS-DID) estimator. Mechanism analyses demonstrate a dual “push–pull” dynamic: Green Credit Intensity (GCI) acts as the primary mediating channel by directing targeted financial resources (financial pull), while stringent environmental regulation positively moderates this effect (administrative push). Furthermore, the moderating role of digital finance is statistically non-significant, underscoring the policy’s broad inclusiveness and its independence from regional digital infrastructure. Heterogeneity estimations identify a clear structural catch-up effect, with more pronounced benefits observed in resource-dependent regions and areas with historically lower innovation capacities. Ultimately, these findings indicate that coordinating targeted financial incentives with environmental oversight can effectively drive multidimensional industrial upgrading, providing valuable evidence for sustainable transition strategies. Full article

Promoting rural industrial integration (RII) is crucial for achieving rural revitalization and agricultural modernization. To empirically evaluate the impact of China’s Rural Industrial Integration Demonstration Park (RIIDP) policy, this study treated the RIIDP policy as a natural experiment, and implemented a staggered difference-in-differences model (DID) to examine the policy’s effects on agricultural modernization and farmers’ income. The empirical findings demonstrate statistically significant positive impacts of the policy on farmers’ income, yielding an average annual increase of 2.1% (equivalent to 234 CNY) per capita and the income-increasing effect has sustained dynamic characteristics. The mechanism analysis reveals that the policy promotes the upgrading of agricultural industries and the improvement of production methods, realizing agricultural modernization and sustainable income growth. Heterogeneity analysis uncovers nuanced spatial and socioeconomic variations in policy effectiveness. The income-increasing effects prove particularly pronounced in counties characterized by lower educational level and greater fiscal capacity. Regional analysis further identifies stronger treatment effects in western and northeastern regions compared to eastern and central regions, suggesting diminishing marginal returns in more developed areas. This study provides empirical evidence on the efficacy of a major rural policy, which is conducive to achieving sustainable rural economic development. The findings provide practical guidance for agricultural transition economies seeking to advance agricultural modernization and increase farmers’ income. Full article

The transformation of trade structures represents a core dimension of economic integration in Central and Eastern Europe, particularly following EU accession and deeper participation in global value chains. Romania and Poland, despite similar institutional frameworks, have exhibited distinct trade trajectories in terms of specialisation patterns, intra–industry trade intensity and external balance. Understanding these differences is essential for assessing the quality of integration, competitiveness and structural upgrading in emerging European economies. Existing empirical studies often focus on single indicators or shorter time horizons, leaving room for a comprehensive, long–run comparative assessment based on multiple trade dimensions. The purpose of this article is to compare the evolution of trade specialisation, intra–industry trade and trade balance in Romania and Poland over the period 2002–2024. The study aims to identify similarities and divergences in their trade structures and to evaluate whether trade expansion has been accompanied by qualitative improvements and external rebalancing. By adopting a comparative perspective, the article seeks to contribute to the literature on trade integration and structural transformation in Central and Eastern Europe. The analysis is based on annual sectoral data on imports and exports for Romania and Poland covering the period 2002–2024. Three complementary indicators are employed: a symmetric Balassa–type revealed comparative advantage index (RSCA), the Grubel–Lloyd intra–industry trade index, and an export–import coverage ratio used as a proxy for sectoral trade balance. Descriptive analysis is complemented by linear trend estimation and structural break tests in order to capture long–run dynamics and identify major shifts associated with EU accession and post–crisis adjustments. The results show that while both countries experienced substantial trade expansion, Poland achieved a significantly stronger qualitative outcome, characterised by higher intra–industry trade intensity and convergence towards aggregate trade balance by 2024. Romania, although recording improvements in trade composition, maintained a persistent trade deficit. The article adds value by providing a long–run, indicator–based comparative framework that integrates specialisation, intra–industry trade and external balance into a single empirical analysis. Full article

Under China’s carbon peaking and carbon neutrality goals, its photovoltaic (PV) sector is transitioning from scale expansion to quality-oriented growth, where intelligent upgrading is essential to improve efficiency, safety, and O&M digitalization. However, its upgrade process in China is severely hampered by a wide range of complex influencing elements. The purpose of this study is to pinpoint and examine the main obstacles to the PV upgrading process as well as clarify their cause–effect relationships to support targeted interventions. Using a mixed-methods approach, we first analyzed interview data from thirty stakeholders using grounded theory to derive barrier categories and factors. The cause-and-effect linkages among these factors were then quantified using the gray-DEMATEL approach. The findings show that funding cost constraints and the lack of incentive mechanisms are the primary and secondary causal factors, respectively, while insufficient R&D capabilities are the most significant resultant factor. The lack of cooperation mechanisms and funding cost constraints were identified as the most comprehensive influencing factors. These findings provide a systematic decision-making framework for policymakers and industry stakeholders to formulate targeted strategies for accelerating PV intelligent upgrading in China. Full article

Technology upgrades are a central lever for sustainability, yet many optimization models primarily account for use-phase emissions and treat embodied impacts and technological change exogenously. We propose a multi-period mixed-integer optimization framework that couples upgrade timing, technology choice, and operations with a life-cycle assessment (LCA) structure. The model (i) separates use-phase and embodied impacts at the transition level, (ii) supports time-weighted valuation of impacts through a flexible weighting sequence (time value of carbon), and (iii) incorporates endogenous learning-by-doing that can reduce both investment costs and embodied impacts of future upgrades. We derive an exact Benders (L-shaped) decomposition that separates discrete upgrade dynamics from a linear operating subproblem. Computational experiments illustrate model behavior and report runtimes under an outer-loop implementation with open-source solvers, highlighting that decomposition becomes most beneficial when extensions substantially enlarge the dispatch layer (e.g., scenario expansion). Experiments also show that ignoring embodied impacts can mis-rank upgrade schedules and even violate life-cycle caps, that stronger time-weighting pushes upgrades earlier, and that learning can make staged upgrades economically preferable. Full article

It is necessary to find sustainable alternatives to the conventional fossil fuels used by the transportation sector today. For the hard-to-abate aviation and heavy transport, liquid hydrocarbon fuels derived from biomass via pyrolysis are a viable option. Biomass pyrolysis oils need upgrading by hydroprocessing before they can be further processed into fuels at a refinery. Due to reactor plugging and catalyst deactivation in one-step hydroprocessing, it has been proposed to add a stabilization step at a lower temperature to convert the most reactive compounds in pyrolysis oil, such as carbonyls, to less reactive species such as alcohols. Three different catalysts, Ni/Al₂O₃, sulfided NiMo/Al₂O₃, and Pt/Al₂O₃, were studied for stabilizing three different model compounds, furfural, guaiacol, and octanoic acid, alone and as a mixture in a batch reactor at 90 bar initial H₂ pressure and 180 °C. The order of performance was determined to be Ni/Al₂O₃ > Pt/Al₂O₃ > sulfided NiMo/Al₂O₃ in these conditions. The Ni/Al₂O₃ catalyst showed both the highest overall conversion, the most fully hydrogenated compounds, and the highest carbonyl conversion. The effect of adding 1172 wt-ppm sulfur to the feed was also investigated, which showed that Ni/Al₂O₃ was the most sensitive catalyst to sulfur poisoning. Full article

Reconciling economic growth with environmental protection continues to represent a central global challenge. As one of the world’s largest developing economies, China has advanced an ecological civilization strategy that offers a unique opportunity to evaluate how national policy can shape sustainable development trajectories. This study assesses whether China’s ecological civilization construction enhances urban green total factor productivity (GTFP). Using panel data for 283 Chinese cities (2006–2019), this study identifies ecological civilization pilot cities through a standardized and reproducible protocol, measures urban GTFP using the Global Malmquist–Luenberger (GML) index and estimates policy effects with a multi-period difference-in-differences (DID) design that accounts for staggered implementation and overlapping policies. The results indicate that urban GTFP exhibited an overall upward but fluctuating trend during the study period, with regional growth rates ranking East > Central > West and a tendency toward convergence in recent years. The analysis further indicates that national ecological civilization construction policies exert a statistically significant and positive effect on urban GTFP, with the findings remaining robust to parallel trend tests and multiple robustness checks. The promotion effect displays marked regional heterogeneity, being strongest in western cities, followed by eastern and central regions, and remains positive across different urban contexts, including resource-based and non-resource-based cities as well as cities within and outside the Yangtze River Economic Belt. Mechanism analysis further reveals that the policy effect operates primarily through industrial upgrading and green technological innovation, whereas the industrial structure rationalization channel is not statistically significant. Overall, this study provides a transparent and reproducible framework for pilot city identification and causal evaluation, offering policy-relevant insights for differentiated and region-specific ecological governance aimed at balanced regional development, industrial upgrading, and green technological innovation. Full article

To address the limitations of conventional anaerobic digestion (AD), this study explored the integration of microbial electrolysis cells (MECs) with AD to improve biogas production and process stability. While AD is a proven technology for renewable energy recovery from waste, it can suffer from volatile fatty acid accumulation and reduced efficiency. The hybrid MEC–AD system leverages electro-methanogenesis to enhance methane yields and overall system performance. This research evaluated the effects of different electrode materials (graphite plate vs. graphite felt) and applied voltages (0.5 V and 0.7 V) on biogas output, methane content, and operational stability. Results showed that MEC–AD systems significantly outperformed conventional AD, with the highest biogas production reaching 239 ± 3 mL/gVS·d—an increase of up to 162% using graphite felt electrodes at 0.5 V. Internal resistance was also markedly lower with graphite felt (19 Ω/m²) compared to graphite plates (1120 Ω/m²). Furthermore, the pH of the MEC–AD system with graphite felt electrodes was maintained within the optimal range (6.8–7.0), avoiding the acidification seen in control systems. These findings underscore the promise of MEC–AD systems for advancing circular bio-economy initiatives and carbon neutrality. Further work is needed to refine electrode materials and reactor design for improved scalability and efficiency. Full article

Efficient scheduling of co-rail waste cranes is critical for ensuring continuous incinerator operation and reducing energy costs in waste-to-energy plants. Existing scheduling methods fail to address the unique characteristics of waste crane operations like task heterogeneity and dynamic spatial interference. To address this, a mixed-integer linear programming model is established to minimize the total crane traveling distance and task delays. A two-stage Discrete Ivy-Simulated Annealing (DIVY-SA) algorithm is proposed: the Ivy algorithm (IVYA) is discretized to generate high-quality task sequences, which are then refined by Simulated Annealing (SA) via a fine-grained local search. A heuristic task assignment scheme and a discrete-event simulation module are designed to evaluate task sequences accurately. Experiments using real-world operational data from a waste incineration plant cover task scales of 25 to 200, representing scheduling horizons of 15 min to 2 h. The algorithm’s runtime (15.04–652.81 s) demonstrates computational feasibility for near-real-time scheduling via a rolling horizon strategy. Results show that DIVY-SA outperforms representative metaheuristic algorithms and reduces the average total traveling distance by 22.19% compared with manual scheduling. This work provides technical support for the intelligent upgrading of waste incineration plants, effectively cutting energy consumption and improving operational efficiency. Full article