Search Results (66)

(1) Background: Each generation’s approach to private-sphere environmentalism is shaped by distinct historical and socio-economic contexts, values, educational opportunities, and living conditions. The aim of this article is to identify differences on this issue among four generations (BB, X, Y, Z) of Poles. (2) Methods: An online survey was conducted on a quota sample of 1000 individuals, with each generation represented by 250 participants. The sample was balanced across generations in terms of gender, education, and place of residence. (3) Results: The top private-sphere environmental behavior was waste segregation (M = 5.1, SD = 1.23), followed by using reusable bags (M = 4.92, SD = 1.2) and reducing energy use (M = 4.57, SD = 1.2). The older the generation, the higher the score in the private-sphere environmentalism index (F = 33.3 (3, 996), p < 0.001). Significant predictors of the private-sphere environmental behaviors (PSE) index were age, gender, environmental concern, and perceived self-impact on the environment, and the final hierarchical regression model explained 38% of the variance in the PSE index. (4) Conclusions: These results underscores the need to account for generational contexts when developing behavior-change strategies and sustainability policies aligned with SDG 12. Full article

Dynamic canal-system scheduling faces the fundamental challenge of determining the optimal reduction in the current period’s water allocation to reserve sufficient water for remaining periods, thereby hedging against potentially greater future water shortages. Although forecast information has been widely incorporated to address this hedging problem, its effectiveness is heavily dependent on forecast accuracy. Integrating abundant historical canal scheduling data with forecast information provides a promising pathway to improve scheduling performance, yet relevant studies remain limited. This study introduces the concept of Target Residual Lump-Sum Water Quota (TRLSWQ) for each time interval and develops a novel “Bi-level, Two-stage” (BT) model for dynamically updated canal-system scheduling that jointly leverages TRLSWQ and forecast information. The model defines clear canal scheduling rules and effectively adapts to the hierarchical structure in canal system scheduling. The model is applied to the summer–autumn irrigation scheduling of the Yongji main canal and six associated sub-canals in the Hetao Irrigation Area, Inner Mongolia, China. The results indicate that compared with the conventional model, the BT model reduces the total water shortage index of sub-canals from 40.81 to 31.44 (a decrease of 22.9%) and increases the utilization rate of the water quota from 89.3% to 92.9% (an increase of 3.9%). Furthermore, this study clarifies the mechanism of canal scheduling deviations caused by forecast errors: early-stage rainfall under-forecasting induces excessive early-stage allocation, leaving no water for later periods, whereas early-stage over-forecasting leads to withheld early allocation and unused residual lump-sum quota in later stages. The BT model effectively balances shortage risks between current and future periods and offers a practical and robust strategy for improving dynamic canal scheduling in irrigation districts. Full article

To address carbon cost pressures from the CBAM and fluctuation in China’s domestic carbon-pricing mechanism, it is crucial to design a carbon quota allocation scheme for China’s steel industry that balances total quantity control and structural optimization. This study comprehensively considers the industry’s LCA and regional heterogeneity, introduces indicators related to “energy transformation contribution”, and employs the maximum deviation method and harmony allocation model to calculate and evaluate provincial quotas and their performance. Results show that: (1) Under the national total quantity control strategy, China’s steel industry carbon quota will be reduced to 1770 Mt by 2030; (2) Calculated via the maximum deviation method, the energy transformation contribution index accounts for 18.95% of the total contribution of all factors, while the LCA index accounts for 46.61%; (3) The maximum inter-provincial difference in carbon quotas reaches 175 Mt, reflecting significant heterogeneity in ecological carrying capacity, resource-allocation efficiency, and emission-reduction potential across regions. This study provides a scientific basis for optimizing China’s unified carbon-market mechanism and guiding the steel industry’s energy transition, and offers a reference for developing countries to address international carbon barriers through regionally differentiated strategies. Full article

This study establishes a synergistic Texture–Quota–Salinity (T–Q–S) model to optimize soil water–salt dynamics in arid agricultural systems. Key findings reveal a sand content threshold (S₀ = 45.2%) governing salt transport efficiency: (1) Sandy soils (S > 50%) exhibit high leaching capacity, enabling the use of saline water (4 g·L⁻¹) with a 270 mm quota to achieve >75% desalination. (2) Threshold soils (S ≈ 45.2%) balance leaching and retention, maximizing nutrient conservation under brackish water (2 g·L⁻¹) and 260 mm irrigation. (3) Clayey soils (S < 30%) require freshwater (≤2 g·L⁻¹) and reduced quotas (≤230 mm) to mitigate surface salinization. The S₀ threshold enables precise irrigation strategies: deep leaching in sandy soils, balanced management in threshold soils, and salt-suppression in clayey soils, enhancing water efficiency by 25% while controlling root zone salinity. Full article

Sustainable development, which integrates economic progress with environmental stewardship to serve societal needs, seeks a balanced approach to resource utilization and intergenerational equity. Implementing carbon policies to limit emissions in production is an effective measure that also puts pressure on the supply chain’s profitability. Meanwhile, the emergence of the price reference effect affects consumers’ behavior and the decisions of supply chain members. This study constructs a dual-channel supply chain model under three carbon policy scenarios within a manufacturer-led Stackelberg game framework. The model is solved analytically to examine equilibrium outcomes and investigate the influence of channel competition, the price reference effect, and carbon policies on profitability and carbon emissions across different scenarios. The results are as follows. (1) As consumers’ online channel preference increases, manufacturers’ profits turn from falling to rising, especially under a lower carbon tax (higher carbon quota), with profit growing earlier. (2) A stronger price reference effect encourages higher emission reduction efforts, selling prices, and profits in smaller markets. However, this effect can reduce prices and profits due to increased competition and pricing pressure in larger markets. (3) The influence of carbon tax and emission quota on emission reduction and price depends on the initial carbon emission of the product, and their interaction has different impacts on total profits at different initial emission levels. (4) Within the mixed policy, the supply chain can obtain better economic and environmental benefits at a specific range of basic market demand. This study provides valuable references for formulating tactics to cope with low-carbon demand and price reference effects, as well as for developing effective environmental protection policies. Full article

The multi-timescale optimization dispatch method for integrated energy systems proposed in this paper balances sustainability and low-carbon characteristics. It first incorporates shared energy storage resources such as electric vehicles into system dispatch, fully leveraging their spatiotemporal properties to enhance dispatch flexibility and rapid response capabilities for integrating renewable energy and enabling clean power generation. Second, an incentive-penalty mechanism enables effective interaction between the system and the green certificate–carbon joint trading market. Penalties are imposed for failing to meet renewable energy consumption targets or exceeding carbon quotas, while rewards are granted for meeting or exceeding targets. This regulates the system’s renewable energy consumption level and carbon emissions, ensuring robust low-carbon performance. Third, this strategy considers the close coordination between heating, cooling, and electricity demand response measures with the integrated energy system, smoothing load fluctuations to achieve peak shaving and valley filling. Finally, through case study simulations and analysis, the advantages of the multi-timescale dispatch strategy proposed in this paper, in terms of economic feasibility, low-carbon characteristics, and sustainability, are verified. Full article

Against the background of carbon neutrality, the impact of carbon quota allocation mechanism on supply chain’s decision-making of emission reduction has received increasing attention. This study analyzes the optimal decision-making behavior of manufacturing supply chains under three mechanisms: completely free, complete auction and hybrid. Meanwhile, the abatement cost-sharing contract is introduced and the backward induction method is applied to solve the optimal equilibrium solution under each mechanism. Combined with numerical simulation, this study further investigates the impacts of market demand and cost-sharing coefficient changes on the system profit. The result shows that the abatement cost-sharing contract can significantly improve the level of manufacturers’ abatement and the total profit of the supply chain. Among the mechanisms analyzed, the hybrid mechanism realizes the balance between efficiency and incentives and demonstrates stronger adaptability and policy flexibility. Full article

The Western and Central Pacific Ocean (WCPO), the key global purse seine fishing ground for skipjack tuna (Katsuwonus pelamis), sees frequent ENSO events. These events drastically alter marine ecosystems and fishery resource patterns, complicating fisheries management—given skipjack tuna’s high mobility and sensitivity to marine environmental changes. To address this, the study proposes an improved spatial prediction framework that incorporates the MGWR model to capture environmental changes. The spatial regression results generated by the MGWR model are incorporated as the mean-field input for the BME model. Additionally, the interannual standard deviation of skipjack tuna resources is fed into the BME model as a measure of spatial uncertainty. The results indicate that the mean field and uncertainty field exhibit a strong correlation, with an R² of 0.54, an RMSE of 583.32, an MAE of 377.22, and an ME of 334.77. Compared to the single prediction models BME and MGWR, the MGWR-BME integrated framework has improved R² by 12%, 30%, and 13% in the 2021–2023 predictions, respectively. Additionally, its prediction performance for distinguishing El Niño, La Niña, and normal years has significantly improved, with R² increasing from 0.6 to 0.67 in 2021, from 0.34 to 0.62 in 2022, and from 0.30 to 0.40 in 2023. According to the evaluation results based on Kernel Density Estimation (KDE) curves, the model performs well in fitting low values but shows weaker performance in fitting high values. By applying this approach, we have clarified the multiscale driving mechanisms through which marine environmental heterogeneity affects the distribution of skipjack tuna under ENSO conditions. This insight enables fishery managers to more accurately predict the dynamic changes in skipjack tuna fishing grounds under different climatic scenarios, thereby providing a reliable scientific basis for formulating rational fishing quotas, optimizing fishing operation layouts, and implementing targeted conservation measures—ultimately contributing to the balanced development of fishery resource utilization and ecological protection. Full article

The livestock sector in Serbia has been experiencing a prolonged period of structural and economic challenges, characterized by decreasing animal numbers, low productivity, and reduced competitiveness in both domestic and EU markets. This study analyses the key structural, technological, economic, and policy factors shaping these trends to provide strategic recommendations for sustainable sector revitalization. The methodology integrates macroeconomic analysis, agricultural economic accounts, and international trade data, applying regression modelling to examine relationships between domestic food prices, exchange rates, and agri-food import volumes. The results indicate that livestock’s share of agricultural gross value added remains below 35%, significantly lower than EU averages, while export quotas remain underutilized and the trade balance for animal products is persistently negative. Contributing factors include fragmented farm structures, outdated production technologies, limited adoption of innovations, demographic decline in rural areas, and insufficient alignment with EU CAP Strategic Plans and Green Deal objectives. Climate change impacts, such as droughts and heat stress, alongside animal disease outbreaks and macroeconomic pressures, further exacerbate these vulnerabilities. The study recommends modernizing production systems through investment in technological upgrades, strengthening farmer organizations and cooperatives, enhancing biosecurity and animal welfare standards, and improving policy frameworks to align with EU sustainability objectives. Emphasis is placed on developing integrated approaches that simultaneously address productivity, economic resilience, and environmental sustainability. Implementing these strategic measures is essential for enhancing food security, supporting rural development, and ensuring Serbia’s successful integration into the EU market as part of a more sustainable and resilient agri-food system. Full article

China, facing severe saline–alkali land degradation, is grappling with the paradox of technically adequate but systemically deficient land consolidation. In response to the existing evaluation system’s over-reliance on physicochemical indicators and neglect of socioeconomic value, this study proposes the use of the Optimal Land Use Value (OLV) to construct a comprehensive benefit evaluation indicator system for saline–alkali land consolidation that encompasses ecosystem resilience, supply–demand balancing, and common prosperity. Considering a case project implemented from 2019 to 2022 in the Western Songnen Plain of China—one of the world’s most severely affected soda saline–alkali regions—this study combines the land use transition matrix with a comprehensive evaluation model to systematically assess the effectiveness and sustainability of land consolidation. The results reveal systemic deficiencies: within ecological spaces, short-term desalination succeeds but pH and organic matter improvements remain inadequate, while ecosystem vulnerability increases due to climate fluctuations and grassland conversion. In production spaces, cropland expansion and saline land reduction are effective, but water resource management proves unsustainable. Living spaces show improved infrastructure and income but face threats due to economic simplification and intergenerational unsustainability. For the investigated case, recommendations include shifting from technical restoration to systemic governance via three strategies: (1) biological–engineering synergy employing green manure to enhance soil microbial activity; (2) hydrological balancing through groundwater quotas and rainwater utilization; (3) specialty industry development for rural economic diversification. This study contributes empirical evidence on the conversion of saline–alkali land, as well as an evaluation framework of wider relevance for developing countries combating land degradation and pursuing rural revitalization. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. Full article

In the context of global climate governance and the low-carbon energy transition, virtual power plant (VPP), a key technology for integrating distributed energy resources, is urgently needed to solve the problem of decentralization and lack of synergy in electricity, carbon, and green certificate trading. Existing studies mostly focus on single energy or carbon trading scenarios and lack a multi-market coupling mechanism supported by blockchain technology, resulting in low transaction transparency and a high risk of information tampering. For this reason, this paper proposes a synergistic optimization strategy for electricity/carbon/green certificate virtual power plants based on blockchain cross-chain transactions. First, Latin Hypercubic Sampling (LHS) is used to generate new energy output and load scenarios, and the K-means clustering method with improved particle swarm optimization are combined to cut down the scenarios and improve the prediction accuracy; second, a relay chain cross-chain trading framework integrating quota system is constructed to realize organic synergy and credible data interaction among electricity, carbon, and green certificate markets; lastly, the multi-energy optimization model of the virtual power plant is designed to integrate carbon capture, Finally, a virtual power plant multi-energy optimization model is designed, integrating carbon capture, power-to-gas (P2G) and other technologies to balance the economy and low-carbon goals. The simulation results show that compared with the traditional model, the proposed strategy reduces the carbon emission intensity by 13.3% (1.43 tons/million CNY), increases the rate of new energy consumption to 98.75%, and partially offsets the cost through the carbon trading revenue, which verifies the Pareto improvement of environmental and economic benefits. This study provides theoretical support for the synergistic optimization of multi-energy markets and helps to build a low-carbon power system with a high proportion of renewable energy. Full article

This paper develops a nonlinear optimization model for the optimal allocation of labor and investment resources in a three-sector open economy. The model is based on the Cobb–Douglas production function and incorporates sectoral interdependencies, capital depreciation, trade balances, and import quotas. The resource allocation problem is formalized as a constrained optimization task, solved analytically using the Lagrange multipliers method and numerically via the golden section search. The model is calibrated using real statistical data from Kazakhstan (2010–2022), an open resource-exporting economy. The results identify structural thresholds that define balanced growth conditions and resource-efficient configurations. Compared to existing studies, the proposed model uniquely integrates external trade constraints with analytical solvability, filling a methodological gap in the literature. The developed framework is suitable for medium-term planning under stable external conditions and enables sensitivity analysis under alternative scenarios such as sanctions or price shocks. Limitations include the assumption of stationarity and the absence of dynamic or stochastic features. Future research will focus on dynamic extensions and applications in other open economies. Full article

Optimizing irrigation and nitrogen (N) management in saline soils is critical for sustainable cotton production in arid regions that have been subjected to climate change. In this study, a two-year factorial field experiment (3 salinity levels × 3 N rates × 3 irrigation quotas) is integrated with the RZWQM2 model to (1) identify water–N–salinity thresholds for cotton yield and (2) to project climate change impacts under SSP2.4-5 and SSP5.8-5 scenarios (2031–2090) in Xinjiang, China, a global cotton production hub. The results demonstrated that a moderate salinity (6 dS/m) combined with a reduced irrigation (3600 m³/hm²) and N input (210 kg/hm²) achieved a near-maximum yield (6918 kg/hm²), saving 20% more water and 33% more fertilizer compared to conventional practices. The model exhibited a robust performance (NRMSE: 5.94–12.88% for soil–crop variables) and revealed that warming shortened the cotton growing season by 1.2–9.5 days per decade. However, elevated CO₂ (832 ppm by 2090) levels under SSP5.8-5 increased yields by 22.6–42.1%, offsetting heat-induced declines through enhanced water use efficiency (WUE↑27.5%) and biomass accumulation. Critically, high-salinity soils (9 dS/m) required 25% additional irrigation (4500 m³/hm²) and a full N input (315 kg/hm²) to maintain yield stability. These findings provide actionable strategies for farmers to optimize irrigation schedules and nitrogen application, balancing water conservation with yield stability in saline-affected arid agroecosystems that have been subjected to climate change. Full article