The development and utilization of wind energy has alleviated the problems of energy shortage and environmental pollution; however, it caused many negative impacts due to suboptimal site selections. This study proposes an innovative method integrating Geographic Information System (GIS), fuzzy Analytic Hierarchy Process (FAHP), and fuzzy VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) for site selection of wind farms in the Wafangdian region, China. The uncertainties caused by subjective judgments of the stakeholders were tackled by the FAHP method firstly, where weight values of six criteria were identified. Next, the fuzzy VIKOR method and GIS tool were used to generate the Qi value of each location for ranking their appropriate degrees for wind energy development. The results demonstrated that the middle and upper parts of the studied area are suitable for the exploitation of wind energy, while the central and eastern areas are unfavorable. The influences exerted by various weight combinations and climate change on a site suitability assessment were examined. The resulting comparison with existing wind farms reflected the practicability and reliability of the proposed method; the estimation of climate change impacts on site selection provided the suggestion and support of a long-term plan for wind power development, and even the energy structure adjustment scheme adapted to climate change.

In this paper, a stochastic multi-objective chance-constrained programming model (SMOCCP) was developed for tackling the water supply management problem. Two objectives were included in this model, which are the minimization of leakage loss amounts and total system cost, respectively. The traditional SCCP model required the random variables to be expressed in the normal distributions, although their statistical characteristics were suitably reflected by other forms. The SMOCCP model allows the random variables to be expressed in log-normal distributions, rather than general normal form. Possible solution deviation caused by irrational parameter assumption was avoided and the feasibility and accuracy of generated solutions were ensured. The water supply system in the Xiaoqing River watershed was used as a study case for demonstration. Under the context of various weight combinations and probabilistic levels, many types of solutions are obtained, which are expressed as a series of transferred amounts from water sources to treated plants, from treated plants to reservoirs, as well as from reservoirs to tributaries. It is concluded that the SMOCCP model could reflect the sketch of the studied region and generate desired water supply schemes under complex uncertainties. The successful application of the proposed model is expected to be a good example for water resource management in other watersheds.

Rapid increases in the amounts of untreated municipal solid waste (MSW) and discharged greenhouse gases (GHGs) from waste treatment processes have caused great challenges to urban development and environmental protection. This study aimed to develop an integrated interval-stochastic optimization model to support regional MSW management in China, where the parameters which show large variations were expressed as birandom variables, whereas the parameters with small fluctuation were assumed to be interval numbers. The main objective of this optimization model was to minimize the total cost with simultaneous considerations of MSW treatment and GHG control requirements. The stochastic equilibrium-based chance constrained programming technique and interactive two-step algorithms were used to solve this model. Among many types of solutions with various constraint-violation levels, the solutions with balanced characteristics were recommended as the decision basis. Compared with existing management schemes, the model solutions showed advantages in cost reduction and climate-change impact mitigation.

Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations.

This study presents a global assessment of future cropland exposure to drought risks under climate change, accounting for both climate variability and land-use changes across income groups. We used SPI and SPEI to assess concurrent 3- and 6-month drought risks during major crop-growing periods, applying a copula-based method to capture joint and transitional drought events. Exposure disparities and dominant drivers were evaluated using high-resolution global cropland projections under SSP245 and SSP585 scenarios. Results show modest increases in SPI-based severe drought concurrence but substantial increases in extreme droughts, particularly under SSP585. SPEI-based projections reveal even greater risks due to temperature-driven evapotranspiration, with some exposure increases exceeding 4000%. Transitional drought risks further intensify vulnerabilities, especially in lower-middle-income countries. Climate change emerges as the dominant driver of exposure increases, while the role of land-use change diminishes. These findings underscore the urgent need for climate mitigation and targeted adaptation to safeguard global food security.