Despite the great potential of mitigating carbon emission, development of wind farms is often opposed by local communities due to the visual impact on landscape. A growing number of studies have applied nonmarket valuation methods like Choice Experiments (CE) to value the visual impact by eliciting respondents' willingness to pay (WTP) or willingness to accept (WTA) for hypothetical wind farms through survey questions. Several meta-analyses have been found in the literature to synthesize results from different valuation studies, but they have various limitations related to the use of the prevailing multivariate meta-regression analysis. In this paper, we propose a new meta-analysis method to establish general functions for the relationships between the estimated WTP or WTA and three wind farm attributes, namely the distance to residential/coastal areas, the number of turbines and turbine height. This method involves establishing WTA or WTP functions for individual studies, fitting the average derivative functions and deriving the general integral functions of WTP or WTA against wind farm attributes. Results indicate that respondents in different studies consistently showed increasing WTP for moving wind farms to greater distances, which can be fitted by non-linear (natural logarithm) functions. However, divergent preferences for the number of turbines and turbine height were found in different studies. We argue that the new analysis method proposed in this paper is an alternative to the mainstream multivariate meta-regression analysis for synthesizing CE studies and the general integral functions of WTP or WTA against wind farm attributes are useful for future spatial modelling and benefit transfer studies. We also suggest that future multivariate meta-analyses should include non-linear components in the regression functions.

Suboptimal land management practices are degrading soils and undermining food production. Sustainable land management (SLM) practices can improve soil and enhance yields. This study identifies variations in SLM uptake, characterising farmers most likely to use SLM practices, identifying when it makes economic sense for farmers to implement particular SLM practices and how long it takes before benefits exceed costs. Using questionnaire data from farmers in western Kenya, we undertake a cost–benefit analysis and analyse determinants of SLM practice use. SLM implementation varied between counties and SLM practice(s), with household and farm characteristics, and access to assets and advice, playing a key role. SLM practices with high upfront and maintenance costs (e.g., terraces and agroforestry) offer low benefit‐to‐cost ratios for individual farmers who must also wait many years to break even on their investments. Nevertheless, over the policy‐relevant time horizon considered (to 2030), Net present value can be positive. Simple SLM practices (manuring and intercropping) have low input costs and offer high benefit to cost ratios, providing a positive net present value up to 2030. Findings suggest that simple practices should be prioritised within policy to improve soil and increase yields. These should be supported by subsidies or other economic measures, facilitating uptake of practices such as agroforestry, which can provide wider societal benefits (e.g., improved water retention and carbon sequestration). Economic mechanisms could be augmented with support for agricultural innovation systems, improved monitoring of land management and yield relationships, and investment in climate and soil information services.

Coupled with rapid urbanization and urban expansion, the spatial relationship between transportation development and land use has gained growing interest among researchers and policy makers. In this paper, a complex network model and land use intensity assessment were integrated into a spatial econometric model to explore the spatial spillover effect of the road network on intensive land use patterns in China’s Beijing–Tianjin–Hebei (BTH) urban agglomeration. First, population density, point of interest (POI) density, and aggregation index were selected to measure land use intensity from social, physical, and ecological aspects. Then, the indicator of average degree (i.e., connections between counties) was used to measure the characteristics of the road network. Under the hypothesis that the road network functions in shaping land use patterns, a spatial econometric model with the road network embedded spatial weight matrix was established. Our results revealed that, while the land use intensity in the BTH urban agglomeration increased from 2010 to 2015, the road network became increasingly complex with greater spatial heterogeneity. The spatial lag coefficients of land use intensity were positively significant in both years and showed a declining trend. The spatially lagged effects of sector structure, fixed asset investment, and consumption were also significant in most of our spatial econometric models, and their contributions to the total spillover effect increased from 2010 to 2015. This study contributes to the literature by providing an innovative quantitative method to analyze the spatial spillover effect of the road network on intensive land use. We suggest that the spatial spillover effect of the road network could be strengthened in the urban–rural interface areas by improving accessibility and promoting population, resource, and technology flows.