Municipal Scale Research Articles

Modelling the long-term sustainability impacts of coordination policies for urban infrastructure rehabilitation

Due to structural and hydraulic deterioration, urban water pipe networks have annual rehabilitation needs. Worldwide, these needs are often significantly larger than the actual amount of rehabilitation being performed, leading to increased risks of serious failures, lower performance and a growing techno-financial burden for future generations. It is well accepted that, in order to limit the multiple impacts of utility works in the urban environment, rehabilitation projects should be coordinated between water, transport, energy and telecommunication infrastructures. In practice, such coordination means that public utilities must rehabilitate assets earlier or later than technically needed, in order to engage in joint projects in which digging and resurfacing expenditures are shared. Hence, at the municipal scale, such coordination influences two variables that are key to strategic decision support: average costs (€/metre) for asset rehabilitation, and the service lifetimes of those assets. However, current models for strategic asset management do not enable practitioners to estimate how changes in the coordination process may influence the long-term financial and environmental impacts of infrastructure rehabilitation. The present study aimed at addressing this methodological gap by introducing the concept of a coordination window that quantifies to what extent utilities compromise asset rehabilitation times in order to join multi-utility projects. An algorithm for modelling the influence of the coordination window size on long-term sustainability costs is presented and applied to one Swedish municipality. The results suggested that total capital costs and carbon emissions can be lowered by 34% and 16% with a coordination window of 35 and 25 year, in comparison to the no-coordination case.

Simulation of land use trends and assessment of scale effects on ecosystem service values in the Huaihe River basin, China.

Land use demand change in the Huaihe River basin (HRB) and ecosystem service values (ESVs) in watersheds are important for the sustainable development and use of land resources. This paper takes the HRB as the research object, and using remote sensing images of land use as the data source adopts the comprehensive evaluation analysis method of ESVs based on equivalent factors and sensitivity analysis of the performance characteristics of ESV changes of different land use types. The PLUS model is used to predict spatiotemporal land use change characteristics to 2030 combining inertial development, ecological development, and cultivated land development. The spatial distribution and aggregation of ESVs at each scale were also explored by analyzing ESVs at municipal, county, and grid scales. Considering also hotspots, the contribution of land use conversion to ESVs was quantified. The results showed that (1) from 2000 to 2020, cultivated land decreased sharply to 28,344.6875 km2, while construction land increased sharply to 26,914.563 km2, and the change of other land types was small. (2) The ESVs in the HRB were 222,019 × 1012 CNY in 2000, 235,015 × 1012 CNY in 2005, 234,419 × 1012 CNY in 2010, 229,885 × 1012 CNY in 2015, and 224,759 × 1012 CNY in 2020, with an overall fluctuation, first increasing and then decreasing. (3) The ESVs were 219,977 × 1012 CNY, 218,098 × 1012 CNY, 219,757 × 1012 CNY, and 213,985 × 1012 CNY under the four simulation scenarios of inertial development, ecological development, cultivated land development, and urban development, respectively. At different scales, the high-value areas decreased, and the low-value areas increased. (4) The hot and cold spots of ESV values were relatively clustered, with the former mainly clustered in the southeast region and the latter mainly clustered in the northwest region. The sensitivity of ecological value was lower than 1, while the ESV was inelastic to the ecological coefficient, and the results were plausible. The mutual conversion of cultivated land to water contributed the most to ESVs. Based on the multi-scenario simulation of land use in the HRB by the PLUS model, we identified the spatial distribution characteristics of ESVs at different scales, which can provide a scientific basis and multiple perspectives for the optimization of land use structure and socio-economic development decisions.

International corporations trading Brazilian soy are keystone actors for water stewardship

Transnational corporations play a major, but poorly constrained, role in reallocating global water resources. Here, we couple high-resolution, company-specific trade data with hydrological and crop models to estimate the virtual water trade of the top 9 transnational corporations that trade Brazilian soy. We identify 4429 virtual water flows connecting 1620 Brazilian municipalities with the top-10 soy importing countries and find that the total virtual water flow increased from 43 billion m3 to 100 billion m3 between 2004 and 2018. We find that the largest soy traders displace on average twice as much virtual water as top-importing countries, excluding China. For example, in 2018 one transnational corporation exported 15 Gm3, almost tripling the Netherlands’s virtual water import (the second largest importer at about 5 Gm3). Our findings highlight the importance of transnational corporations for achieving water stewardship and sustainable supply chains to support water resource security at municipal and international scales.

Combining Statistical, Displacement and Damage Analyses to Study Slow-Moving Landslides Interacting with Roads: Two Case Studies in Southern Italy

Slow-moving landslides are widespread natural hazards that can affect social and economic activities, causing damage to structures and infrastructures. This paper aims at proposing a procedure to analyze road damage induced by slow-moving landslides based on the joint use of landslide susceptibility maps, a road-damage database developed using Google Street View images and ground-displacement measurements derived from the interferometric processing of satellite SAR images. The procedure is applied to the municipalities of Vaglio Basilicata and Trivigno in the Basilicata region (southern Italy) following a matrix-based approach. First, a susceptibility analysis is carried out at the municipal scale, using data from landslide inventories and thematic information available over the entire municipalities. Then, the susceptibility index, the class of movement and the level of damage are calculated for the territorial units corresponding to the road corridors under investigation. Finally, the road networks are divided into stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the performed analyses. The results highlight the importance of integrating all of these different approaches and data for obtaining quantitative information on the spatial and temporal behavior of slow-moving landslides affecting road networks.

Post-growth municipalism: exploring the scalar constitution, strategic relevance, and legal viability of the municipal scale for tackling growth dependencies

ABSTRACT This paper examines the role of the “municipal scale” for the institutionalisation of post-growth-oriented arrangements. It develops an approach that foregrounds the everyday making and unmaking of growth dependencies, which it puts into conversation with the relational spatial thinking and trans-institutional orientation of new municipalist scholarship. Weaving together insights from both literatures, the paper outlines what it means to focus transformations beyond growth at the municipal scale, the strategic rationale of and behind this focus, and the legal consequences thereof. In doing so, the paper identifies the municipal scale as a salient entry point to address conceptual and empirical tensions between state- and community-led transformations – a challenge that impinges on post-growth research specifically. Following an analysis of the scalar constitution, strategic relevance, and legal viability of the municipal scale, the paper argues for a more space and scale-sensitive approach to post-growth transformations and policy.

High Temperature Risk Assessment at the Municipal Scale in China

As global warming leads to increases in the frequency and intensity of high temperatures, the negative impacts of high temperatures on human society are becoming increasingly severe. In recent years, heat risk has become the focus of many studies. To effectively address high temperature risks, a high temperature risk evaluation index system was constructed based on Chinese meteorological, demographic, and economic data in three dimensions: hazard, exposure, and vulnerability. The results of this study reveal the spatial pattern of high temperature risks, and identify the main factors contributing to these risks through a contribution model. (1) During 1961–2020, the intensity of high temperatures and the number of high temperature days in China showed a fluctuating upward trend, which was most obvious in the southeast and northwest. (2) The clustering characteristics of high-temperature risk distribution in China were clear, and the high-risk areas were mainly distributed in the southeast and northwest. The urban high-temperature risk values were between 0.00 and 0.50, among which Yuxi, Turpan, Hangzhou, Nanchang, and several other cities had greater high temperature risks. (3) The hazard and vulnerability contributions were the largest among high-risk cities and low-risk cities, respectively. Among the hazard-causing cities, Turpan had the largest hazard contribution; while among the exposure-causing cities, Shenzhen had the largest exposure contribution; and among the vulnerability-causing cities, Pingliang had the largest vulnerability contribution. The findings of this study are of great significance by providing information that will enable an effective response to high-temperature risks and that can be used to strengthen regional disaster prevention as well as mitigation and sustainable economic development.

Therapeutic landscapes, networks, and health and wellbeing during the COVID-19 pandemic: A mixed-methods study among female domestic workers

The COVID-19 pandemic has had profound impacts on access to and use of therapeutic landscapes and networks, especially for people who are vulnerable due to economic, social, and work-related disadvantage. For one such vulnerable population, Indonesian female domestic workers (FDWs) in Hong Kong, this study employed a mixed methods approach to examine the associations between perceptions of therapeutic landscapes (TLs), therapeutic networks (TNs), subjective wellbeing, and self-rated health during the COVID-19 pandemic. Data from an online survey were analyzed via structural equation modeling (SEM) and confirmatory factor analysis (CFA) to investigate the direct and indirect associations between TLs, TNs, and health and wellbeing. The findings demonstrate little or no association among FDWs’ perceptions of TLs and TNs and FDWs’ self-rated health and subjective wellbeing, except for a negative total association between TL and subjective wellbeing. Using insights gleaned from thematic analysis of in-depth interviews with FDWs, we suggest that these unexpected findings are mainly due to restricted access to public places, reduced social gatherings, and the fact that employers rarely granted days off during the lockdown. Although processes at the employer and municipal scales limited FDWs’ access to therapeutic places, increased use of digital communications and spaces provided an important source of social and emotional support during the pandemic.

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

BackgroundThe impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China.MethodsWe descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011–2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020–2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020–2021.ResultsThe weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011–2019, with an obvious winter-spring seasonality in Beijing. During the 2020–2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, “Cancelling public events” and “Restrictions on internal travel” measures played an important role in the reduction of ILI in Beijing, while the “restrictions on international travel” was statistically most associated with ILI reductions in the Hong Kong SAR.ConclusionsOur study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.Graphical

Relationships between COVID-19 and disaster risk in Costa Rican municipalities

The COVID-19 pandemic has had far-reaching impacts on every aspect of human life since the first confirmed case in December 2019. Costa Rica reported its first case of COVID-19 in March 2020, coinciding with a notable correlation between the occurrence of disaster events at the municipal scale over the past five decades. In Costa Rica, over 90% of disasters are hydrometeorological in nature, while geological disasters have caused significant economic and human losses throughout the country's history. To analyze the relationship between COVID-19 cases and disaster events in Costa Rica, two Generalized Linear Models (GLMs) were used to statistically evaluate the influence of socio-environmental parameters such as population density, social development index, road density, and non-forested areas. The results showed that population and road density are the most critical factors in explaining the spread of COVID-19, while population density and social development index can provide insights into disaster events at the municipal level in Costa Rica. This study provides valuable information for understanding municipal vulnerability and exposure to disasters in Costa Rica and can serve as a model for other countries to assess disaster risk.

Influence of local site effects on seismic risk maps and ranking of Italian municipalities

The latest studies concerning seismic risk assessment in Italy were developed without considering the site geo-lithological effects, thus limiting the assessment to rock soil type. In this study, for the evaluation of site amplification effects, we used the results of recent works based on the Italian seismic microzonation data. As this first study is performed for the entire Italian territory at the municipality scale, the site amplification factors (AFs) have been assigned to the chief town of each of the 7715 Italian municipalities, assuming that the building stock is concentrated in that small area. The AFs have been compared with those foreseen by the Italian building code (NTC2018). The PGA amplification with the new AFs ranges from 1 to 2.2 bringing to an average increase of 75% in the hazard, compared to 27% if using the NTC2018 amplification. The seismic risk has been evaluated using the probabilistic hazard assessment adopted in the NTC2018 and recent vulnerability/exposure models developed for Italy. The residential building stock was subdivided into 5 vulnerability classes (3 for masonry and 2 for concrete) derived from the national census data and further refined using the construction age and building height. The results show that, for a rock site condition, the loss estimates are comparable with those of previous works in terms of casualties, homeless, and economic damage. The introduction of the site effects brings to a significant increase in the expected losses resulting higher than the historical data retrieved from a careful analysis of the major Italian earthquakes. This suggests the importance of developing new fragility curves based on updated AFs. To support the implementation of risk reduction programs, a seismic risk index is introduced, allowing a municipality ranking by risk, a cost estimation of the seismic retrofitting in the highest risk municipalities, and an assessment of the consequent risk reduction.

Exploring the Relationship between the Eco-Environmental Quality and Urbanization by Utilizing Sentinel and Landsat Data: A Case Study of the Yellow River Basin

Yellow River Basin urban agglomeration (YRBU) is the main carrier of regional socio-economic development in the Yellow River Basin, and its eco-environmental quality, urbanization, and coupling coordination degree are facing higher demands. It is of great significance for the development of YRBU to understand the interactive coupling relationship between the eco-environment and urbanization development from the multi-scale perspective. This research intended to understand the spatio-temporal characteristics of eco-environmental quality, urbanization, and coupling coordination degree in the study area from 2013 to 2021. We proposed an Adjusted Remote Sensing Ecological Index (A-RSEI), integrated Sentinel-2A, Landsat 8, and other remote sensing data to evaluate the eco-environmental quality of the study area, from 2013 to 2021. Coupled coordination degree (CCD) model was used to obtain the CCD between eco-environmental quality and urbanization. In addition, spatio-temporal and multi-scale analysis was carried out from the perspectives of urban agglomeration, municipal, county, and pixel scales. Combined with spatial autocorrelation analysis and Tapio decoupling model, the CCD was further explored. The results show that the proposed A-RSEI model is more suitable for monitoring the eco-environmental quality of the Yellow River Basin. The coupling coordination degree of eco-environment and urbanization in most regions of the study area are rising in a relatively green development trend. The multi-scale analysis among eco-environmental quality, urbanization, and CCD can not only indicate the impact of the central city on its surrounding areas but also help to describe the details of CCD combined with the terrain. The comprehensive discrimination of urban agglomeration and county scale is helpful to express the relationship between urbanization and eco-environmental quality centered on a certain city. The results can provide scientific support for eco-environment protection and high-quality development of the Yellow River Basin.

Driving factor analysis of spatial and temporal variations in the gray water footprint of crop production via multiple methods: A case for west China

The gray water footprint (GWF) can quantitatively evaluate the effect of non-point pollution on water quality in the context of water quantity. It is crucial to explore the driving forces behind the GWF to solve water quality problems. This study quantified the unit GWFs of grain crops and oil crops at the municipal scale in six provinces of western China over 2001–2018, then jointly applied the extended STIRPAT model and path analysis methods to analyze the climatic and socioeconomic driving forces of the GWF. Results show that the key driving forces affecting the GWF obtained by the two methods were consistent. Planting structure and population were the main factors increasing the total GWF, while crop yield was the largest factor inhibiting the unit GWF and demonstrates regional differences. However, when the indirect influence of the driving factor through other factors was large, some driving forces obtained by different methods were reversed. For example, the indirect impact of per capita cultivated land area on the total GWF in Inner Mongolia was large, resulting in a significant positive impact in path analysis and a slight negative impact in the STIRPAT model. To draw more comprehensive and referential conclusions, we suggest using multiple methods together to verify the driving forces and account for the regional differences.

Livestock density affects species richness and community composition of butterflies: A nationwide study

Extensively managed grasslands are globally recognized for their high biodiversity value. Over the past century, a continuous loss and degradation of grassland habitats has been observed across Europe that is mainly attributable to agricultural intensification and land abandonment. Particularly insects have suffered from the loss of grassland habitats due to land-use change and the decrease in habitat quality, either due to an increase in livestock density, higher mowing frequency, and an increase in nitrogen fertilization, or by abandonment. However, only a few studies have used nationwide datasets to analyse the effects of land cover and land-use intensity on insects. It further remains largely unexplored how these effects are modulated by species traits, i.e. habitat specialisation and mobility. Using nationwide butterfly data originating from the German Butterfly Monitoring Scheme, we investigated the effect of three indicators related to land cover and agricultural land-use intensity on species richness as well as trait composition of butterfly communities. Based on agricultural census data at the municipality scale, we calculated the share of permanent grasslands (measure of habitat availability), the total livestock density (proxy for organic fertilization) and the livestock density of domestic herbivores (proxy for management intensity in grasslands) within a 2 km buffer surrounding each butterfly transect. To analyse the relationships between butterflies and indicators of land cover and land-use intensity, we applied generalised linear mixed effect models. We found a negative relationship between butterfly species richness and the livestock density of domestic herbivores. Further, the ratio of butterfly generalist to specialist species shifted towards generalists and the size of butterflies increased with higher herbivore livestock density, indicating a shift in communities towards mobile habitat generalists. Our results are in accordance with previous studies carried out across smaller geographic extents, highlighting the importance of low herbivore livestock densities to halt the loss of pollinating insects and safeguard biodiversity and associated ecosystem services in agricultural landscapes. We here demonstrate that indicators based on livestock distribution data at the municipality scale can provide insights into processes and spatial diversity patterns of butterflies at the national level. Further, we highlight potentials and limitations of using agricultural census data to quantify and assess effects of land cover and land-use intensity on butterflies, and make recommendations for further research needs.

Spatio-temporal variation of PM2.5 pollution in Xinjiang and its causes: The growing importance in air pollution situation in China

Due to the differences in topographic features, water vapor distribution and emission structures between the north and south of Xinjiang Uygur Autonomous Region (hereinafter Xinjiang), the air pollution exhibits remarkable spatial heterogeneity in this region. In this study, Xinjiang is divided into four regions from north to south at the municipality scale based on the data from air quality monitoring sites recorded from 2013–2019, namely, the clean area in the north of northern Xinjiang (region I), the heavily polluted area of the Urumqi-Changji-Shihezi region (region Ⅲ), the moderately polluted area in the north of southern Xinjiang (region Ⅱ), and the severely polluted area in the south of southern Xinjiang (region Ⅳ). For highly polluted regions in northern and southern Xinjiang, regions Ⅲ and Ⅳ are compared with those typical polluted regions in central and eastern China, and the spatio-temporal variation characteristics and the causes of PM2.5 pollution in each sub-region are discussed. The results show that the region Ⅲ is a typical area with anthropogenic air pollution source, where the occurrence frequency of PM2.5 pollution with the intensity of moderate level or above (18%) is higher than the most heavily polluted area in central and eastern China (16%, regions from the south of North China to the west of Huang-Huai). The region IV is a typical area affected by dust weather, where the pollution frequency being about 52% is much higher than that in other typical polluted regions. In addition, under the combined effects of anthropogenic sources and dust aerosols, the pollution duration in region II is prolonged. Therefore, in contrast to the remarkable improvement of air quality in central and eastern China, the air pollution in typical regions of Xinjiang has become relatively more severe in recent years. Moreover, as Xinjiang is in the upstream of the central and eastern China in the mid-latitude westerlies, the heavy pollution due to sand and dust, anthropogenic sources and their mixing effects has a far-reaching impact on the downstream areas, further highlighting the growing importance of pollution prevention and control in Xinjiang.

京津冀地区生态系统服务对城镇化的多空间尺度动态响应

PDF HTML阅读 XML下载 导出引用 引用提醒 京津冀地区生态系统服务对城镇化的多空间尺度动态响应 DOI: 10.5846/stxb202204181049 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然基金（72004014）；北京社科基金青年项目（19SRC011）；国家社科基金重大项目（22ZDA056） Dynamic responses of ecosystem services to urbanization at multi-spatial scales in the Beijing-Tianjin-Hebei region Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:大规模的城镇化导致了城市不断扩张，给生态系统造成了巨大的压力。以京津冀地区为例，分析了2000-2020年间京津冀地区城镇化与生态系统服务的变化以及二者间的关系，并探讨了这种关系在栅格尺度、县域尺度、市域尺度上的尺度效应以及变化规律。利用InVEST模型对所选取的4项生态系统服务指标进行量化，并运用皮尔逊相关性分析和地理加权回归来检验其与4项城镇化指标间的关系。研究结果表明（1）随着京津冀地区城镇化的发展除碳储量下降了0.8%外其余生态系统服务均有所提升，其中生境质量增加0.8%、水分产量增加了68%、土壤保持能力增长了35.7%而粮食产量增长近三倍。（2）生态系统服务与城镇化间存在显著相关性（P<0.01），且相关性在县域尺度上最为显著。其中产水量与4项城镇化指标均呈现正相关，粮食产量、碳储量、土壤保持量及生境质量均与4项城镇化指标均呈现负相关。（3）地理加权回归结果表明，城镇化指标与生态系统服务仅在县域尺度上拟合度较好且具有明显的聚集趋势，其中城市用地占比与碳储量、粮食产量全域拟合度最高分别为0.42与0.63。在针对生态系统服务的管理上不同区域需要制定不同的政策，同时需要对尺度进行适当的动态调整。文章明确了城镇化对生态系统服务的影响，并强调了城镇化及生态系统服务关系间的尺度效应，为京津冀地区生态系统管理提供了参考。 Abstract:With the rapid acceleration of China's urbanization process, cities across the country are in a rapid development. According to the data from the National Bureau of Statistics in 2021, China's population urbanization rate reached 64.72%. Large-scale urbanization has led to the expansion of cities, which has put enormous pressure on the ecosystems. The urbanization of Beijing-Tianjin-Hebei region (BTH) is the epitome and typical example of rapid urbanization development in China. In 2018, the urbanization rate of BTH has reached 65.8%. With the continuous advancement of urbanization, the natural ecosystem of the BTH has undergone a complex change, which posed a severe challenge to the sustainable development of the region. At the same time, the imbalance of regional development in the BTH is very serious. In this study, we analyzed the changes of urbanization and ecosystem services and the relationship between them in Beijing-Tianjin-Hebei region during 2000-2020. We also explored the scale effects and change patterns of this relationship at the grid scale, county scale, and municipal scale. The four urbanization indicators are nighttime light, urban land proportion, population density and GDP density, while the five ecosystem services indicators are water yield, food provisioning, soil conservation, carbon storage and habitat quality. In this study, the InVEST model was used to quantify the selected ecosystem services indicators, and the results of the four urbanization indicators were obtained by combining statistical data. Based on this, Pearson correlation analysis and geographically weighted regression were applied to test the relationship between them. The results showed that (1) the ecosystem services increased with the urbanization in the BTH, except for carbon storage, which decreased by 0.8%, with habitat quality increased by 0.8%, water yield increased by 68%, soil conservation increased by 35.7%, and food provisioning increased by nearly three times. (2) There was a significant correlation between ecosystem services and urbanization (P< 0.01), and the correlation was the most significant at the county scale. Water yield was positively correlated with the four urbanization indicators, while food provisioning, carbon storage, soil conservation and habitat quality were all negatively correlated with the 4 urbanization indicators. There was also a significant correlation between ecosystem services. Carbon storage, soil conservation and habitat quality showed a trade-off on the three scales. At the same time, this correlation would increase with time. (3) The results of geographically weighted regression showed that the fitting degree of urbanization indicators and ecosystem services was high only on the county scale, and had a clearly spatial agglomeration characteristic. Among them, the fitting degree of urban land proportion, carbon storage and food provisioning was the highest, which were 0.42 and 0.63, respectively. It suggests that using the county scale as the study scale is to some extent a more accurate response to the impact of urbanization on ecosystem services. However, some ecosystem services were more correlated with urbanization indicators at the grid and municipal scales, so different policies needed to be developed for the management of ecosystem services in different regions, and the scales could be adjusted dynamically as appropriate. The article clarified the impact of urbanization on ecosystem services, and focused on the scale effects between urbanization and ecosystem services relationships, providing a reference for ecosystem management in the Beijing-Tianjin-Hebei region. 参考文献 相似文献 引证文献

A social method to address the interplay between protected areas and land use policies

During the last decade, several authors have supported the need to integrate protected areas (PAs) with land use planning (LUP) policies to improve nature conservation and promote sustainable and just cities and territories. However, this process presents multiple challenges in different parts of the world. This article introduces and develops a method of analysis for the articulation between protected areas (PAs) and land use planning (LUP) policies from the perspective of social sciences. This combines a diagnostic first stage of content analysis of land use plans at a provincial/regional/statal scale, with a second stage of an in-depth political analysis in some study cases at municipal scale. The first stage involve the spatial identification of all the municipal districts of a selected study area with provincial, national or international PAs and land use plans. Seven questions guide the analysis of those plans in terms of the degree and quality of PAs nominal articulation. This information, in addition to previous normative data, allows for the development of a formula for the comparative analysis and mapping of the institutional vulnerability and risk of those municipal districts. In the second stage, a synchronous substantive (content) and institutional (network of stakeholders) analysis of land use policies in selected municipal cases is carried out. To this end, interviews, surveys, media analysis and public documents are conducted. Thus, this method combined different focus scales and quali-quantitative techniques to advance the knowledge of key variables intended for improving political coordination in the promotion of nature conservation. This model was tested in the case of the province of Buenos Aires (Argentina).•A social sciences’ method for studying land use planning and protected areas coordination is introduced.•The method combines two stages and scales: diagnostic content analysis of land use plans in a provincial/regional/statal scale plus an in-depth political analysis in study cases at a municipal scale.•This method is useful for estimating normative vulnerability and risk, identifying key political variables for coordination, and making public policy recommendations according to the current conservation paradigm.

Advancing towards a sustainable energy model, uncovering the untapped potential of rural areas

&lt;abstract&gt; &lt;p&gt;Rural areas are essential to moving towards the necessary sustainable energy transition and climate change mitigation through renewable energy (RE) technologies. However, RE planning and decision-making in rural locations have not been developed to date with a focus on the local level and accompanied by a careful and thorough assessment of the simultaneous availability of alternative RE sources in a specific territory. Quite differently, RE investments in rural locations have been primarily driven by the interests of large power utilities to exploit a particular RE source, with benefits escaping from the rural economies to end up in the income statements of those large corporations. There is a need to approach RE planning at the municipal scale considering the availability of alternative RE sources. This study suggests the development of a rural RE potential index that could help in the identification of appropriate locations for the implementation of hybrid renewable energy systems (HRESs). The construction of a composite indicator to measure rural RE potential is exemplified through a case study that deals with ten indicators in the context of Galician rural municipalities, involving different RE potentials and some technical or regulatory constraints. Equal weighting and Principal Component Analysis are considered alternative methods for the index construction. Municipalities are the relevant local decision level where energy policy should be focused in order to diversify both the RE mix and the investor base. The proposed index could be the basis for future analyses aimed at optimizing the design and implementation of HRESs in rural environments at a local-regional-national scale.&lt;/p&gt; &lt;/abstract&gt;

An Italian platform for the seismic risk assessment of school buildings

The safety of school buildings is a highly relevant and topical issue and the earthquake is one of the actions from which they must be “protected”. Many factors affect it, both from the structural and non-structural point of view. To identify the most cost-benefit seismic mitigation program is fundamental to have an overview of the school heritage in our peninsula, the vulnerability and the seismic risk which schools are subjected to. In this context, within the MARS (Seismic Risk Maps) research project funded by the Italian Department of Civil Protection, an online platform developed by EUCENTRE, with the close collaboration of the Network of University Laboratories for Earthquake Engineering (ReLUIS) has been adopted to calculate the seismic risk of school buildings. The platform IRMA (Italian Risk MAps), initially focused only to residential buildings, has been further developed with a specific tool dedicated to the evaluation of seismic risk maps for school buildings at national level. It allows to upload different exposure/vulnerability models and sets of fragility curves and to combine them to obtain seismic risk maps at municipal, provincial, regional and national scale. The hazard integrated in IRMA corresponds to the hazard model adopted by the regulations at national level.

Applying the Multilevel Approach in Estimation of Income Population Differences

Income inequality remains one of the most burning issues discussed in the world. The difficulty of the problem arises from its multiple manifestations at regional and local levels and unique patterns within countries. This paper employs a multilevel approach to identify factors that influence income and wage inequalities at regional and municipal scales in Russia. We carried out the study on data from 2017 municipalities of 75 Russian regions from 2015 to 2019. A Hierarchical Linear Model with Cross-Classified Random Effects (HLMHCM) allowed us to establish that most of the total variances in population income and average wages accounted for the regional scale. Our analysis revealed different variances of income per capita and average wage; we disclosed the reasons for these disparities. We also found a mixed relationship between income inequality and social transfers. These variables influence income growth but change the relationship between income and labour productivity. Our study underlined that the impacts of shares of employees in agriculture and manufacturing should be considered together with labour productivity in these industries.

Avoidable mortality due to long-term exposure to PM2.5 in Colombia 2014–2019

ObjectiveTo compare estimates of spatiotemporal variations of surface PM2.5 concentrations in Colombia from 2014 to 2019 derived from two global air quality models, as well as to quantify the avoidable deaths attributable to the long-term exposure to concentrations above the current and projected Colombian standard for PM2.5 annual mean at municipality level.MethodsWe retrieved PM2.5 concentrations at the surface level from the ACAG and CAMSRA global air quality models for all 1,122 municipalities, and compare 28 of them with available concentrations from monitor stations. Annual mortality data 2014–2019 by municipality of residence and pooled effect measures for total, natural and specific causes of mortality were used to calculate the number of annual avoidable deaths and years of potential life lost (YPLL) related to the excess of PM2.5 concentration over the current mean annual national standard of 25 µg/m3 and projected standard of 15 µg/m3.ResultsCompared to surface data from 28 municipalities with monitoring stations in 2019, ACAG and CAMSRA models under or overestimated annual mean PM2.5 concentrations. Estimations from ACAG model had a mean bias 1,7 µg/m3 compared to a mean bias of 4,7 µg/m3 from CAMSRA model. Using ACAG model, estimations of total nationally attributable deaths to PM2.5 exposure over 25 and 15 µg/m3 were 142 and 34,341, respectively. Cardiopulmonary diseases accounted for most of the attributable deaths due to PM2.5 excess of exposure (38%). Estimates of YPLL due to all-cause mortality for exceeding the national standard of 25 µg/m3 were 2,381 years.ConclusionComparison of two global air quality models for estimating surface PM2.5 concentrations during 2014–2019 at municipality scale in Colombia showed important differences. Avoidable deaths estimations represent the total number of deaths that could be avoided if the current and projected national standard for PM2.5 annual mean have been met, and show the health-benefit of the implementation of more restrictive air quality standards.