Search Results (175)

This paper examines how openness of international trade is dynamically related to environmental sustainability in sixteen member states of the Southern African Development Community (SADC) between 2000 and 2024, taking into consideration institutional quality factors, economic development, and structural factors. The study uses the Panel Fully Modified Ordinary Least Squares (FMOLS), Pedroni panel cointegration tests, and quantile regression to examine the determination of per capita CO₂ emissions by using trade openness, GDP per capita, government effectiveness, energy use, natural resource rents, and urbanisation. The findings of cointegration prove a long-run equilibrium stability. FMOLS estimates show that trade openness positively but insignificantly increases the typically pooled long-run specifications through urbanisation and natural resource rents and negatively through GDP per capita, which is in line with the phase upper-Environmental Kuznets Curve. The outcome of quantile regression reveals a large distributional heterogeneity with the trade openness decreasing emissions only among high-emitting economies at the seventy-fifth and at the ninetieth percentile which is the imperative effect of the quantile technique demonstrating the need for country-differentiated trade and environmental policy across the SADC. Full article

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

The construction sector contributes approximately 39% of global carbon emissions, with embodied carbon—emissions from material extraction, manufacturing, transportation, and construction—representing a systematically underestimated yet increasingly critical component of building life cycle environmental impacts. Traditional Life Cycle Assessment (LCA) methods suffer from static database dependencies, delayed feedback cycles, and limited integration with active construction decision-making, creating a fundamental gap between environmental assessment and construction operations. This paper presents the Digital Twin-Enabled Life Cycle Assessment Framework (DT-LCAF), a dynamic construction-phase embodied carbon accounting system aligned with the EN 15978 standard (stages A1–A5) that integrates Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and machine learning designed to support real-time sustainability decision-making during smart building construction, with computational performance validated through the offline processing of historical datasets. The framework introduces two enabling mechanisms: (1) a Multi-Scale Carbon Prediction Network (MSCPN) employing hierarchical graph attention networks to capture material interdependencies across component, system, and building scales; and (2) a Reinforcement Learning-based Carbon Optimization Engine (RL-COE) that generates constraint-aware recommendations for material substitution, supplier selection, and construction sequencing while respecting structural, economic, and temporal constraints. Experimental evaluation employs two complementary validation strategies using proxy embodied carbon labels (not ground-truth construction measurements): embodied carbon prediction accuracy is assessed using proxy carbon labels derived from the CBECS dataset (5900 commercial buildings) combined with the ICE Database v3.0 emission factors, achieving a 10.24% MAPE, representing a 23.7% improvement over the best-performing baseline in predicting these proxy estimates; temporal responsiveness and streaming data ingestion capabilities are validated using the Building Data Genome Project 2 (1636 buildings, 3053 m). The RL-COE optimization engine demonstrates an 18.4% mean carbon reduction rate within the proxy label framework across building types while maintaining cost and schedule feasibility. A BIM-based case study illustrates the framework’s construction-phase update loop, showing how embodied carbon estimates evolve dynamically as construction progresses. The limitations regarding the proxy-based nature of embodied carbon labels and the absence of ground-truth construction-phase measurements are explicitly discussed. The framework contributes to smart city sustainability by enabling scalable, data-driven embodied carbon intelligence across building portfolios. All quantitative results are based on proxy embodied carbon estimates derived from building characteristics and standard emission factor databases, rather than measured project data. The reported performance therefore demonstrates a proof-of-concept within the proxy system, and real-project, measurement-based validation remains future work. Full article

South Africa’s transport sector remains a major contributor to greenhouse gas emissions, yet limited empirical evidence exists on the environmental drivers of public transport emissions at the provincial level. This study applies an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework within a dynamic panel setting to examine the determinants of provincial public transport emissions across nine South African provinces from 2015 to 2022. Rather than conducting economy-wide emissions accounting, the analysis focuses on transport-specific drivers relevant to public passenger mobility, including population, income, fuel consumption, infrastructure investment, and modal usage. A Bias-Corrected Method of Moments (BCMM) estimator is employed to address emission persistence, endogeneity, and small-sample bias, with pooled ordinary least squares and fixed-effects models used for robustness. Province fixed effects are used to control for unobserved regional heterogeneity, while common dynamic elasticities are estimated for key influencing factors. The results reveal strong dependence on emissions, indicating substantial structural persistence over time. GDP per capita emerges as the dominant and statistically significant driver of public transport emissions, while population, urbanisation, fuel consumption, transport infrastructure investment, and modal usage (road and rail) are statistically insignificant once dynamics and unobserved heterogeneity are controlled. These findings suggest that public transport emissions in South Africa are driven primarily by economic growth and entrenched structural factors rather than short-run changes in transport systems. Policy implications highlight the need for sustained low-carbon investment, technological transition, and integrated transport planning to decouple economic growth from emissions and support progress toward Sustainable Development Goals 11 and 13. Full article

This paper proposes a methodological framework for linking standardized carbon footprint reporting with structured decision support in logistics. The approach integrates the GHG Protocol framework and the ISO 14064 standard in order to formalize emissions inventories, reporting requirements, and verification constraints into a coherent, transparent, and auditable analytical structure. While existing standards provide robust guidance for the quantification and reporting of greenhouse gas emissions, their systematic integration into decision support representations remains limited. The main contribution of the paper consists of the formal operationalization of carbon accounting processes into decision variables, constraints, and performance indicators that preserve traceability, transparency, and compatibility with external verification requirements. A simplified linear programming formulation is employed as a standard-driven decision support abstraction, illustrating how emissions-related data derived from standardized reporting can be consistently translated into operational constraints and analytical indicators. The mathematical formulation is not intended to replace detailed logistics optimization models, but to demonstrate the methodological linkage between emissions reporting, verification requirements, and structured decision-oriented analysis. The proposed framework is illustrated through a logistics hub case study using average emission factors and estimated consumption data. The numerical results serve an illustrative purpose and highlight the functioning of the framework, rather than providing fully calibrated operational solutions. The methodology is designed to be reproducible and auditable and may be extended to other industrial sectors, as well as to more advanced modeling settings incorporating dynamic or stochastic elements. Full article

For less-developed, carbon-dependent regions, achieving carbon decoupling while pursuing economic catch-up presents a fundamental challenge. This study investigates this persistent dilemma through the case of Jiangxi Province, China, a typical coal-reliant inland region. Utilizing data from 2000 to 2022, we estimate carbon emissions following IPCC guidelines and employ the Generalized Divisia Index Method (GDIM) to decompose emission drivers, effectively overcoming the limitation of factor independence in conventional decomposition analyses. The results identify economic scale (cumulative contribution: 97.81%) and energy consumption (51%) as the primary drivers of emission growth, while carbon intensity of output (−47.38%) emerges as the strongest inhibiting factor. The application of the Tapio decoupling model reveals that weak decoupling is the dominant state, prevailing in 91% of the study period. This persistent pattern underscores only a partial and unstable separation between economic growth and emissions, highlighting the region’s entrenched carbon lock-in. Our findings demonstrate that transcending this weak decoupling dilemma necessitates a strategic shift beyond efficiency gains. We propose that the resolution lies in accelerating structural transitions within the energy system and fostering low-carbon industrial upgrading. This study not only elucidates the dynamics of the carbon decoupling challenge in catch-up regions but also offers actionable and context-specific pathways, providing a valuable reference for analogous regions, particularly in developing and transition economies. Full article

This paper analyses the factors that affect CO₂ emissions in the BF-BOF steelmaking process using a dynamic econometric approach based on annual data from the Polish steel industry. The analysis commences with the estimation of a baseline dynamic model that describes the relationship between CO₂ emissions in the industry and investment allocations, crude steel production, and lagged CO₂ emissions. The baseline analysis illustrates the dominant feature of strong emission level persistence and poor tracking of selected conventional production-related factors. The analysis proceeds by extending the baseline results through additional consideration of technological factors, material composition factors, and resource use factors in the generation of CO₂ emissions. The additional factors include the use of coke, electricity consumption, fixed asset value, and the scrap ratio. The analysis indicates that these additional factors are essential in improving the accuracy of the modeling process and in clarifying the significance of material composition in CO₂ emissions in particular. The analysis further illustrates the critical result that increased use of electricity leads to high CO₂ emissions in the BF-BOF process. Further analysis indicates that increasing the use of steel scrap leads to substantial CO₂ reductions in the BF-BOF route and other steelmaking technologies. The results also show that CO₂ emissions in the BF-BOF process depend not only on production volume, but also on material composition and the technological structure of the process. In the context of the WFESF project, these findings provide evidence-based guidance for metal industry research by identifying priority levers for mitigation, particularly through improvements in process technology and scrap-based material substitution. Full article

The growing environmental challenges and the urgent need for an accelerated energy transition have intensified the European Union’s efforts to expand the use of renewable energy sources and reduce dependence on fossil fuels. This study estimates the impact of selected socioeconomic, political, and technological factors on the share of renewable energy in gross final energy consumption. This issue is of key relevance for EU energy and climate policy, particularly in the context of the ongoing transformation processes in Central and Eastern European (CEE) Member States. The analysis covers eleven CEE countries—Poland, the Czech Republic, Slovakia, Hungary, Romania, Bulgaria, Lithuania, Latvia, Estonia, Croatia, and Slovenia—over the years 2004–2023. Using panel data models in both static (fixed effects) and dynamic specifications, the study identifies the determinants of renewable energy development and captures inertia effects. The results reveal strong links between energy security and renewable energy deployment, where the pursuit of greater self-sufficiency often slows the expansion of renewable energy. Countries with high greenhouse gas emissions also show limited incentives to accelerate renewable energy integration. Based on the findings, this study proposes policy recommendations aimed at enhancing energy efficiency, strengthening energy security, and supporting the long-term sustainable growth of renewable energy in the region. Full article

The transition toward renewable energy systems offers significant opportunities to reduce greenhouse gas (GHG) emissions, while also introducing new challenges in risk management and policy design. This study examines the long-term effects of renewable energy consumption, the risk factors associated with environmental taxation, and public expenditure on greenhouse gas (GHG) emissions across 27 European Union countries over a period of 22 years. Using panel data techniques—specifically the Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators—the analysis identifies robust cointegrating relationships among environmental, fiscal, and energy variables. The joint null hypothesis (H₀) states that renewable energy consumption, environmental taxation, and public environmental expenditure do not exert a statistically significant negative long-run effect on greenhouse gas (GHG) emissions in the European Union (i.e., none of these variables contributes to reducing GHG emissions in the long run). The findings show that renewable energy consumption and environmental taxes significantly and negatively affect GHG emissions, confirming their effectiveness as instruments for emission risk mitigation. Pollution taxes display the strongest elasticity among fiscal measures, indicating their pivotal role in carbon reduction strategies. Furthermore, public expenditure, particularly in waste management, meaningfully contributes to long-term emission reductions. These results highlight that a cohesive policy framework combining renewable energy development, targeted taxation, and strategic public investment can effectively minimize the environmental and economic risks associated with decarbonization. The study provides valuable empirical evidence for policymakers and risk analysts, underscoring the importance of integrated fiscal and energy policies in achieving sustainable climate risk management across the European Union Full article

Transportation is the highest emitting sector in the US, and electrifying transportation is an effective way to reduce emissions. However, electrification efforts have typically focused on battery electric vehicles (BEVs); but extended-range EVs (EREVs), EVs with a backup gasoline generator, could play a major role. Nonetheless, reducing transportation-related costs and carbon emissions hinges on understanding how an EREV’s range and charging profile affect electric miles driven and, by extension, emission savings. This study evaluates the distribution of vehicle miles traveled (VMT) between electric and gasoline modes for EREVs across electric range (25–150 miles) and charging frequency scenarios. Using 2023 U.S. trip data by distance and monthly VMT benchmarks, we apply a dynamic mean-distance estimation method to match observed totals and allocate VMT to EV or gasoline power based on trip length. We explore different charging, efficiency, and cost scenarios. Our results show, at current average efficiencies, that EREVs with a 50-mile range (13.7 kWh battery) could electrify 73.3% of national VMT, while 150-mile range EVs could electrify 86.8% illustrating that there are diminishing returns at higher ranges. We also compute corresponding carbon emissions savings using national fuel economy and emissions factors. Results highlight the nonlinear trade-offs between range and emissions reduction. Findings suggest that expanding the EREV range significantly boosts electrification potential up to 100 miles but offers marginal gains beyond. However, if users charge infrequently, larger range EVs are needed to maintain the benefits of vehicle electrification. Our results imply that policymakers and manufacturers should prioritize moderate range EREVs for households who frequently charge (e.g., homeowners) and long range BEVs for infrequent users (e.g., apartment dwellers). Full article

Evaluating the relationship between dynamic carbon emission intensity (CEI) and high-quality economic development (HQED) provides crucial insights for advancing national strategies focused on ecological preservation and sustainable high-quality development. This study employed an integrated analytical framework that combines the entropy-weight TOPSIS model, the coupling coordination degree (CCD) model, the spatial autocorrelation, and a two-way fixed effects model to examine the spatiotemporal patterns and influencing factors of carbon emissions in the Yangtze River Basin from 2010 to 2022. The results indicated that: (1) Temporal analysis revealed a consistent annual decline in CEI levels, coupled with steady improvements in HQED. The coordination between these two systems was reflected in the estimated CCD, and it showed an upward trend, with the lower reaches experiencing the most rapid progress in coordination. (2) Spatial analysis revealed a polycentric development pattern, with Shanghai serving as the central core, and other metropolises such as Nanjing and Hangzhou acting as secondary cores. The high–high agglomeration area has been progressively expanding each year. (3) Analysis of influencing factors revealed that their impacts diminished in the following order: human capital, economic development, urbanization, green innovation, government support, industrial structure, and openness. Each of these influencing factors demonstrated distinct spatiotemporal heterogeneity, varying in their impact across different regions and time periods. The study finally provided recommendations, emphasizing the need for coordinated development strategies in the YREB, taking regional dynamics into account, and promoting green economic transformations while ensuring ecological and environmental sustainability. Full article

The factors influencing the use of renewable energy in ten significant emerging economies (E-10: Argentina, Brazil, China, Indonesia, India, Mexico, Poland, Russia, South Africa, and Turkey) are examined in this study for the years 1990–2021. In order to capture both contemporaneous and intertemporal drivers of renewable energy demand, the analysis uses dynamic panel techniques (GMM) in conjunction with static panel estimations (fixed and random effects), drawing on a balanced panel dataset. The empirical findings highlight the path-dependent character of the energy transition by pointing to a clear persistence effect, in which previous renewable energy consumption significantly and favorably influences current levels. While oil prices and carbon emissions exert adverse pressures, economic growth and financial development are consistently recognized as key facilitators of the adoption of renewable energy. In several specifications, population growth appears as a constraining factor. Both static and dynamic models show that urbanization has a negative impact on the use of renewable energy. Therefore, incorporating renewable energy considerations into urban development policies may help reverse this trend and promote increased use of renewable energy. When combined, the results show how strategically important it is to promote economic growth, strengthen financial systems, and incorporate sustainability into urbanization processes. The urgent need to phase out fossil fuel subsidies, reroute financial resources toward green investment, and fortify carbon mitigation frameworks are among the policy implications. In the end, the evidence favors a multifaceted policy framework for the E-10 nations to hasten the switch to renewable energy. Full article

Tire–road friction is a fundamental factor in vehicle safety, energy efficiency, and environmental sustainability. This narrative review synthesizes current knowledge on the tire–road friction coefficient (TRFC), emphasizing its dynamic nature and the interplay of factors such as tire composition, tread design, road surface texture, temperature, load, and inflation pressure. Friction mechanisms, adhesion, and hysteresis are analyzed alongside their dependence on environmental and operational conditions. The study highlights the challenges posed by emerging mobility paradigms, including electric and autonomous vehicles, which demand specialized tires to manage higher loads, torque, and dynamic behaviors. The review identifies persistent research gaps, such as real-time TRFC estimation methods and the modeling of combined environmental effects. It explores tire–road interaction models and finite element approaches, while proposing future directions integrating artificial intelligence and machine learning for enhanced accuracy. The implications of the Euro 7 regulations, which limit tire wear particle emissions, are discussed, highlighting the need for sustainable tire materials and green manufacturing processes. By linking bibliometric trends, experimental findings, and technological innovations, this review underscores the importance of balancing grip, durability, and rolling resistance to meet safety, efficiency, and environmental goals. It concludes that optimizing friction coefficients is essential for advancing intelligent, sustainable, and regulation-compliant mobility systems, paving the way for safer and greener transportation solutions. Full article

Sustainable development requires integrating economic growth with environmental protection; however, rising carbon emissions pose a substantial threat to ecological balance. The conclusions of this study regarding the determinants of carbon emissions risk within the broader sustainability framework—coal and oil consumption, foreign direct investment (FDI), and economic growth—are critically significant. The application of ARDL and Dynamic ARDL estimate methods indicates that coal and oil consumption, along with foreign direct investment (FDI), exert a considerable and favourable influence on carbon emissions. The Toda–Yamamoto causality study indicates a bidirectional influence between coal usage and carbon emissions. Conversely, oil consumption and foreign direct investment influence carbon emissions solely via coal consumption. These findings underscore the need to develop efficient emission control strategies rapidly. Policy recommendations include accelerating economic restructuring, reducing dependence on fossil fuels, and promoting the adoption of clean, renewable energy sources. By analyzing these factors, the study offers significant insights into achieving simultaneous economic growth and environmental sustainability. Full article

A precise understanding of the carbon dynamics of agricultural land use is essential for advancing China’s “dual carbon” goals and promoting sustainable rural development. Drawing on panel datasets for 31 Chinese provinces over the period 1997–2022, this study comprehensively analyzes the spatiotemporal evolution, regional disparities, and transition dynamics of agricultural carbon capture and emissions. Using a combination of the emission factor method, the Dagum Gini coefficient, kernel density estimation, and Markov chain models, the study finds that China’s total agricultural carbon capture has continued to increase, yet regional disparities are widening, with the central region leading and the northeastern region lagging. Meanwhile, agricultural carbon emissions exhibit a “strong west, weak east” spatial pattern and demonstrate a high degree of club convergence. Club convergence refers to the phenomenon where regions with similar initial levels converge to the same steady-state over the long run, while remaining persistently different from other regions. The net carbon effect exhibits a dual structure of carbon surplus zones and carbon deficit zones: 23 provinces act as carbon surplus zones, while 8 provinces are carbon deficit zones, primarily located in ecologically fragile or special-function regions. These findings highlight the spatial heterogeneity, path dependence, and policy sensitivity of carbon effects from agricultural land use. Accordingly, the study proposes differentiated policy recommendations, including region-specific carbon management strategies, the establishment of a unified agricultural carbon trading system, and the integration of technological and institutional innovations to achieve a balanced and low-carbon agricultural transformation. Full article