Search Results (2,212)

The study compares the overall sustainability of two organic and two conventional rice farming systems during the 2022 drought. The research aimed to develop an experiment exploring the ability of an integrated methodological approach to identify tradeoffs and provide actionable insights for a sustainable agricultural transition under extreme climate stress. To this aim, the study employed economic analysis, Life Cycle Assessment (LCA) for environmental impact, and the OASIS framework for broader social and resilience indicators. The study revealed tradeoffs between the economic efficiency of conventional rice farming and the ecological resilience of organic systems, a conclusion made possible only through its integrated assessment methodology. By combining different methods, the research suggested that while conventional farms achieved clear financial superiority and greater efficiency per ton of rice, organic systems showcased superior ecological performance per hectare, greater biodiversity, and enhanced resilience. This highlights a crucial research frontier focused on designing hybrid systems or new economic models that can translate the environmental resilience of organic methods into tangible market value, effectively resolving the very tradeoffs this comprehensive assessment suggested. Full article

Multivariate time series forecasting is crucial for numerous practical applications ranging from financial markets to climate monitoring. Traditional multivariate time series forecasting methods primarily adopt a time-centric modeling paradigm, applying attention mechanisms to the temporal dimension, which presents significant limitations when handling complex dependencies between variables. To better capture inter-variable interaction patterns, this paper proposes the Variable-Centric Transformer (VCformer), which shifts the attention paradigm from time-centric to variable-centric through sequence transposition. Building upon this foundation, we further design a dual-scale architecture that simultaneously models feature representations at both the original variable level and variable group level. Combined with an adaptive variable grouping mechanism, we construct a parameter-sharing dual-path encoder and finally select the optimal feature fusion strategy through comparative experiments. Experimental results on seven benchmark datasets demonstrate that VCformer achieves comprehensive improvements in prediction accuracy compared to traditional time-centric methods, while exhibiting stronger modeling capabilities on high-dimensional data. Ablation studies and interpretability analysis further validate the effectiveness of each component. Full article

Energy raw materials are the basis of the economic system. From this emerges the need to examine in more detail how various uncertainty indices interact with the dynamic of spillover connectedness among energy markets. The TVP-VAR model is used to investigate connectedness among US, European, and Indian oil and gas markets and the S&P carbon allowances Eua index. Following this, the wavelet decomposition technique is used to capture the dynamic correlations between uncertainty indices (GPR and VIX) and connectedness indices. First, the results indicate that energy market spillovers are time-varying and crisis-sensitive. Second, the time–frequency dependence among uncertainty indices and connectedness indices is more marked and can change with the occurrence of unexpected events and geopolitical conflicts. The VIX index shows a positive dependence on total dynamic connectedness in the mid-long-term, while the GPR index has a long-term effect only after 2020. The analysis of the interdependence among the connectedness of each market and the uncertainty indices is more heterogeneous. Political tensions and geopolitical risks are, therefore, causal factors of energy prices. Given their strategic and economic importance, policy makers and investors should establish a risk warning mechanism and try to avoid the transmission of spillovers as much as possible. Full article

The objective of this study was to analyze price variability and the factors influencing the formation of monthly prices of by-products of the wood industry in Poland between October 2017 and January 2025. The analysis considered the impact of economic variables, including energy commodity prices (natural gas and coal) and industrial wood prices, on the pricing of wood industry by-products. The adopted approach enabled the identification of key determinants shaping the prices of these by-products. The effectiveness of two tree-based regression models—Random Forest (RF) and CatBoost (CB)—was compared in the analysis. Although RF offers greater interpretability and lower computational requirements, CB proved more effective in modeling dynamic, time-dependent phenomena. The results indicate that industrial wood prices exerted a weaker influence on by-product prices than natural gas prices, suggesting that the energy sector plays a leading role in shaping biomass prices. Coal prices had only a marginal impact on the biomass market, implying that changes in coal availability and pricing did not directly translate into changes in the prices of wood industry by-products. The growing role of renewable energy sources derived from natural gas and wood biomass is contributing to the emergence of a distinct market, increasingly independent of the traditional coal market. In Poland, due to limited access to alternative energy sources, biomass plays a critical role in the decarbonization of the energy sector. Full article

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article

Climate change poses significant challenges to the economic and social sustainability of urban dwellers, particularly in the real estate market, where rising temperatures are affecting property values. While most research focuses on how climate change impacts buyers and sellers, this study shifts attention to renters, who may be more vulnerable to climate-induced price increases. By analyzing rental price and climate data, this study uses ordinary least squares (OLS) and fixed-effects regressions to assess the impact of temperature fluctuations on rental rates across 50 major U.S. metropolitan areas. The findings reveal a positive and significant relationship between rising temperatures and rental rates, particularly in the Northeastern and Southern U.S. These results suggest that targeted policy interventions may help ease financial pressures on vulnerable renters and support more sustainable urban development over time. The analysis also highlights the potential role of energy efficiency measures in rental housing to lower energy costs and alleviate rent burdens. Additionally, the findings indicate that local policymakers may consider rent stabilization strategies and investments in urban green infrastructure to protect low-income renters, reduce localized heat exposure, and promote long-term urban resilience. Full article

Sustainable energy entrepreneurs promote sustainable development by focusing more on energy efficiency. This study examines the interdependence and driving–dependent relationships among the socioeconomic factors (SEFs) influencing sustainable energy enterprises (SEEs). A mixed-methods approach is used, beginning with a literature review and expert consensus, followed by total interpretive structural modeling (TISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. Seven key SEFs are finalized through interviews with 12 experts. Data are then collected from 11 SEEs. The study reveals that the regulatory and institutional framework emerges as the primary driving factor influencing other SEFs, including financial accessibility, market demand, technological innovation, and infrastructure readiness. Social and cultural acceptance is identified as the most dependent factor. The study proposes future research directions by identifying the United Nations sustainable development goals (SDGs) related to the antecedents, decisions, and outcomes with theoretical linkages through the Antecedents–Decisions–Outcomes (ADO) framework. The major SDGs identified are SDG 4 (education), SDG 7 (energy), SDG 9 (industry), SDG 11 (communities), and SDG 13 (climate). The study highlights that regulatory support, funding access, skill development, and technology transfer are required areas for strategic focus. Understanding the hierarchy of SEs supports business model innovation, investment planning, and risk management. Full article

Electric vehicle (EV) charging stations play a pivotal role in the widespread adoption and integration of electric vehicles into mainstream transportation systems. While the effects of climate change and greenhouse gases are increasing worldwide, the transition to electric vehicles is of high importance in terms of both ecological and sustainability. EV charging stations serve as the backbone of this transition, providing essential infrastructure to support the charging needs of EV owners and facilitate the transition to electric vehicles. In this study, a MINLP mathematical model is developed for the multi-objective optimization of EVCS. For implementation, Istanbul’s European side and a large-scale synthetic case are addressed considering both current demand and estimations for low, medium, and high EV numbers by the Energy Market Regulatory Authority (EMRA) for 2030 and 2035. The primary aim is to minimize station numbers, capacity, waiting time, and station idle time while meeting the demand. During the solvation of the mathematical model, both present demand and future EV usage forecasts are taken into consideration. This involves simulating different scenarios using EMRA’s 2030 and 2035 estimates and determining the optimal locations and capacities for charging stations for each demand level. Efficiencies in different scenarios were evaluated and the created mathematical model provides to optimize EV charging stations in multiple ways, there will be savings in total cost and labor force. The findings of the study will provide a valuable guide to the EV charging station infrastructure planning of the highways, regions, and urban areas to be selected in possible studies. The multi-directional optimization model addressed in this study will support decision-makers and industry experts in making informed decisions towards the sustainable and efficient development of EV charging infrastructure. Full article

This paper presents a novel integration of bioclimatic-passive architectural elements—Trombe walls, pergolas, and deciduous climbers—in the context of residential buildings in Eastern and Central Europe, a combination that remains largely underexplored in the current literature. The innovativeness of the proposed concept is reflected in the combined use of the following building elements: three types of passive Trombe wall (single-glazed, double-glazed, and triple-glazed), pergolas, and four types of deciduous climbers (V. coignetiae, H. lupulus, W. sinensis, and A. macrophylla). By using meteorological data for the towns Kragujevac and Kielce, the influence of location parameters for two dominant European climate zones (moderate continental and continental) is also included in this investigation. The initial single-family building models were created following the Serbian and Polish rulebooks on energy efficiency for new buildings and equipped with the same thermo-technical systems and people occupancy conditions. Based on the conducted simulations (using Google SketchUp 8 and EnergyPlus 7.1) and obtained results on the annual level, the following main conclusions can be drawn: (1) a moderate continental climate is more suitable for implementing the proposed concept; (2) a single-glazed passive Trombe wall is not energy or environmentally justified; (3) the energy, environmental, and economic benefits for both selected locations are greatest in the case of the combined use of pergolas, V. coignetiae, and triple-glazed passive Trombe wall; and (4) before the wider commercial application of the proposed concept in the future, efforts should be made to explore economic opportunities, which, among other things, involve a focus on market stability and accessibility. Full article

The outlook for biobased plastics in packaging applications is increasingly promising, driven by a combination of environmental advantages, technological innovation, and shifting market dynamics. Derived from renewable biological resources, these materials offer compelling benefits over conventional fossil-based plastics. They can substantially reduce greenhouse gas emissions, are often recyclable or biodegradable, and, in some cases, require less energy to produce. These characteristics position biobased plastics as a key solution to urgent environmental challenges, particularly those related to climate change and resource scarcity. Biobased plastics also demonstrate remarkable versatility. Their applications range from high-performance barrier layers in multilayer packaging to thermoformed containers, textile fibers, and lightweight plastic bags. Notably, all major fossil-based packaging applications can be substituted with biobased alternatives. This adaptability enhances their commercial viability across diverse sectors, including food and beverage, pharmaceutical, cosmetics, agriculture, textiles, and consumer goods. Several factors are accelerating growth in this sector. These include the increasing urgency of climate action, the innovation potential of biobased materials, and expanding government support through funding and regulatory initiatives. At the same time, consumer demand is shifting toward sustainable products, and companies are aligning their strategies with environmental, social, and governance (ESG) goals—further boosting market momentum. However, significant challenges remain. High production costs, limited economies of scale, and the capital-intensive nature of scaling biobased processes present economic hurdles. The absence of harmonized policies and standards across regions, along with underdeveloped end-of-life infrastructure, impedes effective waste management and recycling. Additionally, consumer confusion around the disposal of biobased plastics—particularly those labeled as biodegradable or compostable—can lead to contamination in recycling streams. Overcoming these barriers will require a coordinated, multifaceted approach. Key actions include investing in infrastructure, advancing technological innovation, supporting research and development, and establishing clear, consistent regulatory frameworks. Public procurement policies, eco-labeling schemes, and incentives for low-carbon products can also play a pivotal role in accelerating adoption. With the right support mechanisms in place, biobased plastics have the potential to become a cornerstone of a sustainable, circular economy. Full article

The BRICS countries have been increasingly prioritizing electricity transition as a crucial step towards achieving sustainable growth, energy security, and mitigating climate change. As major emerging economies, the BRICS nations will play a significant role in the global energy landscape since their transition to renewable energy sources holds a significant implication for global energy markets and environmental sustainability. This study investigates the impact of the renewable energy transition on economic growth in BRICS nations from 1990 to 2023, employing a panel NARDL, DOLS, and FMOLS models. This study investigates the relationship between disaggregated renewable energy sources and economic growth. The findings show that renewable energy’s impact on economic growth varies across countries and depends on the type of renewable energy source. Specifically, hydropower, and wind power are found to have significant positive impacts on economic growth in some BRICS countries, while other renewables and trade openness have insignificant impacts. To foster economic growth and the expansion of renewable energy, it is essential for policymakers to focus on investments in hydropower and wind energy. Furthermore, they should encourage trade liberalization, as well as nuclear power development, and enhance regional collaboration. This study offers significant contributions to the current body of literature on the renewable energy–economic growth nexus, supplying crucial insights for both policymakers and researchers. Full article

Optimizing agricultural structure serves as a crucial pathway to promote sustainable rural economic development. This study focuses on a representative village in the mountainous region of North China, where agricultural production is constrained by perennial low-temperature conditions, resulting in widespread adoption of single-cropping systems. There exists an urgent need to enhance both economic returns and risk resilience of limited arable land through refined cultivation planning. However, traditional planting strategies face difficulties in synergistically optimizing long-term benefits from multi-crop combinations, while remaining vulnerable to climate fluctuations, market volatility, and complex inter-crop relationships. These limitations lead to constrained land productivity and inadequate economic resilience. To address these challenges, we propose an integrated decision-making approach combining stochastic programming, robust optimization, and data-driven modeling. The methodology unfolds in three phases: First, we construct a stochastic programming model targeting seven-year total profit maximization, which quantitatively analyzes relationships between decision variables (crop planting areas) and stochastic variables (climate/market factors), with optimal planting solutions derived through robust optimization algorithms. Second, to address natural uncertainties, we develop an integer programming model for ideal scenarios, obtaining deterministic optimization solutions via genetic algorithms. Furthermore, this study conducts correlation analyses between expected sales volumes and cost/unit price for three crop categories (staples, vegetables, and edible fungi), establishing both linear and nonlinear regression models to quantify how crop complementarity–substitution effects influence profitability. Experimental results demonstrate that the optimized strategy significantly improves land-use efficiency, achieving a 16.93% increase in projected total revenue. Moreover, the multi-scenario collaborative optimization enhances production system resilience, effectively mitigating market and environmental risks. Our proposal provides a replicable decision-making framework for sustainable intensification of agriculture in cold-region rural areas. Full article

Photovoltaic (PV) modules undergo comprehensive testing to validate their electrical and thermal properties prior to market entry. These evaluations consist of durability and efficiency tests performed under realistic outdoor conditions with natural climatic influences, as well as in controlled laboratory settings. The overall performance of PV cells is affected by several factors, including solar irradiance, operating temperature, installation site parameters, prevailing weather, and shading effects. In the presented study, three distinct PV modules were analyzed using a sophisticated large-scale steady-state solar simulator. The current–voltage (I-V) characteristics of each module were precisely measured and subsequently scrutinized. To augment the analysis, a three-layer artificial neural network, specifically the multilayer perceptron (MLP), was developed. The experimental measurements, along with the outputs derived from the MLP model, served as the foundation for a comprehensive global sensitivity analysis (GSA). The experimental results revealed variances between the manufacturer’s declared values and those recorded during testing. The first module achieved a maximum power point that exceeded the manufacturer’s specification. Conversely, the second and third modules delivered power values corresponding to only 85–87% and 95–98% of their stated capacities, respectively. The global sensitivity analysis further indicated that while certain parameters, such as efficiency and the ratio of V_oc/V, played a dominant role in influencing the power-voltage relationship, another parameter, U, exhibited a comparatively minor effect. These results highlight the significant potential of integrating machine learning techniques into the performance evaluation and predictive analysis of photovoltaic modules. Full article

Over recent decades, coastal wetlands in Jiangsu Province have faced multiple challenges, including overfishing, reclamation for aquaculture, wetland shrinkage, and biodiversity loss. Implementing wetland ecological restoration proves crucial for mitigating the degradation of coastal wetland ecosystems. Quantifying ecosystem service values and establishing rational ecological compensation standards provide essential references for ecological compensation research and alleviating human–land conflicts. The Jianchuan Ecological Restoration Project, located in Dafeng District of Yancheng City, Jiangsu Province, employs integrated wetland, woodland, and farmland construction to rebuild biodiversity, enhance water conservation capacity, and improve water purification functions, thereby significantly boosting regional ecological services. Results have demonstrated that the total ecosystem service value of this project reaches CNY 76.2896 million, with climate regulation representing the highest value (CNY 68.1496 million, 89.33% of total). Subsequent values include biodiversity maintenance (3.40%), water purification (3.31%), and food production (2.95%), while carbon sequestration/oxygen release (0.96%) and soil retention (0.05%) show relatively lower contributions. Notably, this project innovatively integrates carbon finance mechanisms through “carbon sink loans”, achieving efficient transformation of ecological value from “paper accounts” to market realization. This study establishes a scientific foundation for ecological restoration projects through ecosystem service-based value assessment and pathway exploration, offering both theoretical framework and practical references. Full article

Kazakhstan, a fossil-fuel-dependent economy, faces growing pressure to reduce greenhouse gas emissions while maintaining industrial competitiveness. Carbon Capture, Utilization, and Storage (CCS/CCUS) technologies offer a viable pathway for decarbonizing hard-to-abate sectors, particularly in power generation, metallurgy, and oil and gas processing. This paper provides a comprehensive review of the state of CCS/CCUS technologies globally and examines their applicability within Kazakhstan. The study also explores long-term CO₂ storage mechanisms and monitoring frameworks, with attention to carbon leakage risks and the importance of addressing methane emissions. A critical part of the analysis is dedicated to Kazakhstan’s Emissions Trading System, identifying its current limitations such as low carbon prices, and limited sectoral coverage, and outlining practical reforms to enhance its role in supporting CCS/CCUS and broader decarbonization efforts. The integration of CCS/CCUS with a strengthened ETS, combined with access to international climate finance instruments and voluntary carbon markets, is proposed as a key strategy for Kazakhstan’s transition to a low-carbon economy. By linking engineering innovation with targeted policy interventions, this study offers a dual-perspective contribution. It not only provides technical insights into CCS/CCUS technologies but also presents policy recommendations that are specifically tailored to Kazakhstan’s context. The findings reinforce the role of CCS/CCUS as a crucial component of national climate strategy and industrial transformation. Full article