Climatic and Economic Background Determine the Disparities in Urbanites' Expressed Happiness during the Summer Heat.

Climate-change-induced extreme weather events increase heat-related mortality and health risks for urbanites, which may also affect urbanites' expressed happiness (EH) and well-being. However, the links among EH, climate, and socioeconomic factors remain unclear. Here we collected ∼6 million geotagged tweets from 44 Chinese prefecture-level cities based on Sina Weibo and performed a quadratic regression model to explore the relationships between summer heat and EH. A three-stage analysis was developed to examine spatiotemporal heterogeneity and identify factors contributing to disparities in urbanites' EH. Results show that all cities exhibited a similar hump-shaped relationship, with an overall optimal temperature (OT) of 22.8 °C. The estimated OT varied geographically, with 25.3, 23.8, and 20.0 °C from north to south. Moreover, a 1 standard deviation increase in heatwave intensity was associated with a 0.813 (95% CI: 0.177, 1.449) standard deviation decrease in EH. Notably, within the geographic scope of this study, it was observed that urbanites in northern China and economically underdeveloped cities faced significantly lower heat risks during the summer heat. This research provides insight for future studies and practical applications concerning extreme weather events, urbanites' mental health, and sustainable urban development goal.

Pursuing implementation solutions for targeted nitrogen management in agriculture- a novel approach to synthesize knowledge and facilitate sustainable decision making based on collaborative landscape modelling.

Regulation of nitrate emission from agriculture to aquatic environments in Denmark currently depend on general rules for nutrient application and associated farm-level reporting schemes. Similar or comparable centralized regulation instruments dedicated to controlling and limiting rather than focusing and improving N application practices exist in large parts of the OECD. Recent policy debates have exposed an array of problems relating to this type of regulation. Problems include issues of appropriate scale, transparency and failures to adapt intervention and regulation to relevant geo-ecological variations in contexts where general rules are being imposed on varied agro-ecosystems. Therefore it has been proposed to rescale regulation to better fit relevant socio-political and agro-environmental processes including the scale of farmers' decision making, the scale of relevant hydrological systems and the scale of key agro-ecological conditions such as soil characteristics and drainage. However, the challenge of shifting the regulation to a more local scale raises a number of questions. These include (1) How information produced locally can be integrated with national scale data? (2) In what way integrated datasets can used to model environmental effects of current and possible land use patterns? (3) In what way data and estimates of consequences of land use changes are best made available in decision making processes? To address these questions this article reports on ongoing work in Denmark to develop a decision support tool for N-management at the scale of agricultural landscapes, which are areas where a similar pattern of land use is repeated across the land surface, reflecting a specific mode of adapting agriculture to natural conditions. The aim of the article is to evaluate the design of a decision support tool aiming at enabling strategic N-management at landscape scales by linking decision support at the scale of individual farms with decision support targeted at groups of farms where a coordinated effort to solve common problems may be more efficient. Design targets for the tool were established empirically based on evidence from exploratory workshops with farmers and other stakeholders in 6 case areas across Denmark. On this basis a prototype GIS-tool for capturing, storing, editing, displaying and modelling landscape scale farming practices and associated emission consequences was developed. The tool was designed to integrate locally held knowledge with national scale datasets in live scenario situations through the implementation of a flexible, uniform and editable data model for land use data - the dNmark landscape model. Based on input data that is corrected and co-authored by workshop participants, the tool estimates the effect of potential land use scenarios on nutrient emissions. The tool was tested in 5 scenario workshops in case areas in Denmark in 2016, on the basis of which its design is evaluated and discussed in this article.

Spatiotemporal patterns and characteristics of remotely sensed region heat islands during the rapid urbanization (1995-2015) of Southern China.

Urban agglomeration has become the most salient feature of global urbanization in recent decades, while spatiotemporal patterns and evolution remain poorly understood in urban agglomerations, which limit the decision-makers to make more informed decisions to improve the regional environment. Here we selected one of the most rapidly urbanized regions in the world - Pearl River Delta Metropolitan Region (PRDR), located in southern China, as the case. Landsat images spanning from 1995 to 2015 were used to retrieve land surface temperature (LST). Four types of regional heat island (RHI) degree were defined for further analysis. Then multi-scale spatiotemporal patterns and characteristics of RHI were identified with the help of cloud-based computing, spatial and landscape analysis. We found that (1) traditional urban heat island (UHI) appears as an RHI on an urban agglomeration scale. In PRDR, we found RHI expended with increasing connectivity, especially in the estuary areas where isolated UHI gradually merged during the rapid urbanization. (2) The contribution of main cities in PRDR to RHI and the evolutionary trends and pattern, which is changed from a west-east to a southwest-northeast gradient, have been revealed. (3) Considering the scale effect and different RHI categories, we revealed that during the urbanization, the aggregation of the RHI is significant on a larger-scale, and the area of 4 °C ≤ Relative LST ≤ 8 °C is the stable and high-risk area, which provide scientific bases for the governance of the thermal environment on the regional scale. (4) The study also indicates the cooling effect of forests and water is better than that of grassland, while the cooling effect of grassland is uncertain. The methods and results of this study not only have implications on environmental planning and management in the PRDR but also provide useful insights into the thermal environment research and practice in other urban agglomerations.

Strong contributions of local background climate to the cooling effect of urban green vegetation.

Utilization of urban green vegetation (UGV) has been recognized as a promising option to mitigate urban heat island (UHI) effect. While we still lack understanding of the contributions of local background climate to the cooling effect of UGV. Here we proposed and employed a cooling effect framework and selected eight typical cities located in Temperate Monsoon Climate (TMC) and Mediterranean Climate (MC) demonstrate that local climate condition largely affects the cooling effect of UGV. Specifically, we found increasing (artificial) rainfall and irrigation contribute to improving the cooling intensity of grassland in both climates, particularly in the hot-dry environment. The cities with high relative humidity would restrict the cooling effect of UGV. Increasing wind speed would significantly enhance the tree-covered while weakening the grass-covered UGVs' cooling effect in MC cities. We also identified that, in order to achieve the most effective cooling with the smallest sized tree-covered UGV, the area of trees in both climate zones' cities should generally be planned around 0.5 ha. The method and results enhance understanding of the cooling effect of UGVs on larger (climate) scales and provide important insights for UGV planning and management.

Spatio-temporal modeling of the invasive potential of wild boar--a conflict-prone species-using multi-source citizen science data.

Denmark was considered not to have an established population of free-ranging wild boar. Today, sporadic observations of wild boar challenge that view. Due to its reservoir role for economic devastating swine diseases, wild boar represents a potential threat for Denmark's position as a large pig- and pork-exporting country. This study assessed the prospects of wild boar invasion in Denmark. Multi-source citizen science data of wild boar observations were integrated into a multi-modelling approach linking habitat suitability models with agent-based, spatially-explicit simulations. We tested whether the currently observed presence of wild boar is due to natural immigration across the Danish-German border, or whether it is more likely that wild boar escaped fenced premises. Five observational data sources served as evaluation data: (1) questionnaires sent to all 1625 registered owners of Danish farm land, located in the 60 parishes closest to the border, (2) an online questionnaire, (3) a mobile web-based GPS application, (4) reports in the media or by governmental agencies, and (5) geo-referenced locations of fenced wild boar populations. Data covering 2008-2013 included 195 observations of wild boar, including 16 observations of breeding sows. The data from the Danish Nature Agency and the mailed questionnaires were consistent regarding the location of wild boar observations, while data from the Danish Veterinary and Food Administration, the media and the electronic questionnaires documented individual scattered observations in the rest of Jutland. Most observations were made in the region bordering Germany. It is uncertain whether the relatively few observations represent an established population. Model outcomes suggested that the origin of wild boar in about half of the area with sporadic observations of wild boar could be attributed to spatial expansions from a local Danish population near the border and consisting of wild boar originally of German origin. However, the other half, located distant to the border, were likely a result of animals escaping fenced premises inside the country. The approach serves as a template to assess the status of an invading species and improve the knowledge base for risk assessment and management decision.

Current use of impact models for agri-environment schemes and potential for improvements of policy design and assessment.

Agri-Environment Schemes (AES) to maintain or promote environmentally-friendly farming practices were implemented on about 25% of all agricultural land in the EU by 2002. This article analyses and discusses the actual and potential use of impact models in supporting the design, implementation and evaluation of AES. Impact models identify and establish the causal relationships between policy objectives and policy outcomes. We review and discuss the role of impact models at different stages in the AES policy process, and present results from a survey of impact models underlying 60 agri-environmental schemes in seven EU member states. We distinguished among three categories of impact models (quantitative, qualitative or common sense), depending on the degree of evidence in the formal scheme description, additional documents, or key person interviews. The categories of impact models used mainly depended on whether scheme objectives were related to natural resources, biodiversity or landscape. A higher proportion of schemes dealing with natural resources (primarily water) were based on quantitative impact models, compared to those concerned with biodiversity or landscape. Schemes explicitly targeted either on particular parts of individual farms or specific areas tended to be based more on quantitative impact models compared to whole-farm schemes and broad, horizontal schemes. We conclude that increased and better use of impact models has significant potential to improve efficiency and effectiveness of AES.