Comparative research on NIMBY risk acceptability between Chinese and Japanese college students

Along with the progressive acceleration of urbanization, the need to identify potentially troublesome “Not In My Back Yard” (NIMBY) facilities in the city is inevitable. To resolve NIMBY conflict, it is important to know people’s NIMBY risk acceptability for these facilities. A questionnaire survey was used among Chinese and Japanese college students to identify NIMBY risk acceptability. LISREL was used to construct a structural equation model to analyze the difference in NIMBY risk acceptability between the Chinese and Japanese college students. Factors that may affect NIMBY risk acceptability were analyzed: “perceiving utility,” “perceiving risk,” “trust in government,” “reasonable compensation,” and “procedural justice.” The findings show that Japanese students’ concerns were greater than Chinese students’ concerns. Perceiving utility and perceiving risk were the most important factors that affect people’s NIMBY risk acceptability, followed by procedural justice, trust in government, and reasonable compensation. There is a difference between the different cultural backgrounds in confronting the risk: Chinese students focus more on the reputation and value of real estate, while Japanese students pay more attention to environmental pollution and damage to health. Furthermore, cultural influences play a role in students’ risk perception. To improve the risk acceptability for NIMBY facilities and provide a basis for resolving NIMBY conflicts, it is necessary to ensure the benefits of the NIMBY facility while reducing environmental pollution. The findings of this study may be of interest for policy makers and practitioners to devise future NIMBY strategies.     

Assessment of online public opinions on large infrastructure projects: A case study of the Three Gorges Project in China

Public opinion becomes increasingly salient in the ex post evaluation stage of large infrastructure projects which have significant impacts to the environment and the society. However, traditional survey methods are inefficient in collection and assessment of the public opinion due to its large quantity and diversity. Recently, Social media platforms provide a rich data source for monitoring and assessing the public opinion on controversial infrastructure projects. This paper proposes an assessment framework to transform unstructured online public opinions on large infrastructure projects into sentimental and topical indicators for enhancing practices of ex post evaluation and public participation. The framework uses web crawlers to collect online comments related to a large infrastructure project and employs two natural language processing technologies, including sentiment analysis and topic modeling, with spatio-temporal analysis, to transform these comments into indicators for assessing online public opinion on the project. Based on the framework, we investigate the online public opinion of the Three Gorges Project on China's largest microblogging site, namely, Weibo. Assessment results present spatial-temporal distributions of post intensity and sentiment polarity, reveals major topics with different sentiments and summarizes managerial implications, for ex post evaluation of the world's largest hydropower project. The proposed assessment framework is expected to be widely applied as a methodological strategy to assess public opinion in the ex post evaluation stage of large infrastructure projects.     

Evolution of online public opinions on social impact induced by NIMBY facility

Public opinions play a pivotal role in public participation and decision-making in mega construction projects. The rise of technology and social media has facilitated the online public debate on social impacts derived from mega construction projects. This study presents a novel approach to analyze the evolution of public opinions through social media. This analysis framework covers: the temporal patterns of online messages, evolution of frames, and evolution of users. Such novel approach is employed to analyze the evolution of online public opinions on a mega construction project. Content analysis and a number of curve fitting methods were innovatively combined. Results show that social event had dual diffusion cycles while the formation mechanism of each cycle was completely different. The key parameters of Bimodal Gaussian model can depict the characteristics of online diffusion of public opinions. Elites and media organizations form the crucial nodes in the online diffusion process. The sequence of each frame and their share in the total number of frames are determined by preference of elites and media organizations. These findings are useful to develop effective strategy for public engagement, and to prevent and intervene the social conflicts.     

Can we integrate climatic modelling and assessment?

The climatic change “debate” seems to be expanding faster than the available information base. National and international response strategies (mitigation and adaptation) are being discussed in board rooms, governments and the media. Phrases like “the uncertainty in the scenarios” appear to carry meaning but the information seems to be, at best, incomplete and, at worst, totally debased by multiple definitions and usages. “Scenarios” are predictions of possible futures. The “(un)certainty” that characterizes them encompasses prejudices, perspectives, incompleteness of information as well as the differences in completeness, accuracy and sensitivity of different forecasting tools. (Un)certainty is the source of experimentation and learning but excessive (un)certainty is noise and so the search for a signal requires its reduction. This reduction process can both promote planning and co-ordination but also quash unconventional, but ultimately valuable, ideas. Balancing this tension across the modern, multifaceted climatic “divisions” of technology, training, information access, understanding, terminology, economic development, societal flexibility and social and political perceptions is difficult. The current dysfunctioning of the international climatic information “industry” demands improved communication among these diverse communities. This paper discusses some of the difficulties as perceived by a physical scientist turned “gatekeeper” and proposes a twofold strategy for improving the situation: a climatic change information exchange and an explicit recognition of the need for cyclic social co-operation among the participating groups.     

Exploring the trade-off between benefit and risk perception of NIMBY facility: A social cognitive theory model

With reference to Social Cognitive Theory, this study contributes to the debate on large-scale energy projects that can be classified as NIMBY developments and their implementation in order to better understand drivers to public acceptance. Public acceptance is essential for the development of typical NIMBY facilities. This study draws on Social Cognitive Theory (SCT) to investigate the determinants of public acceptance of a NIMBY facility in Wuhan City, China, to explore how the trade-off between the perceived benefit and risk affected public acceptance. Guanshan power substation was selected as the case study, and a questionnaire was presented to 450 local residents. Using a structural equation model, this study revealed that the perceived benefit-risk was positively related to public acceptance. Self-efficacy positively affected public acceptance via the perceived benefit-risk. Furthermore, the social environment exerted a significant positive effect on public acceptance, self-efficacy and perceived benefit-risk. These findings highlight the importance of improving the social environment and taking perceived benefit-risk into account during the decision-making process for similar developments.     

Exploring the “not in my backyard” effect in the construction of waste incineration power plants - based on a survey in metropolises of China

Waste incineration power plants have become the best option in face of the increasing amount of garbage in China's cities. However, constructing waste incineration power plants will cause “not in my backyard” (NIMBY) effect in the public. To identify variables causing this effect and measure the effect quantitatively, we designed and carried out a random survey in four metropolises in China. Of the 3133 questionnaires distributed, 3066 are thought to be effective. Males, persons with higher educational levels, or with higher income, or having underaged children in their families, or thinking their neighborhoods are upscale ones are willing to pay more to avoid the construction of waste incineration power plants in their surroundings than others. In addition, provided that the respondents deem that the construction of waste incineration power plants would have an impact on their lives, they are willing to pay more no matter they own their departments or not. Finally, the estimated willingness to pay (WTP) to avoid the waste incineration power plants is 105 yuan to 133 yuan per month. Hence, the government should treat the NIMBY syndrome well in the construction of waste incineration power plants, with WTP being one perspective to persuade the public.     

Microeconomic measurement of the social costs of environmental regulation

This paper provides a coherent microeconomic framework for defining and measuring the social costs of environmental regulations. A fundamental identity between two alternative means of measuring social costs is established and used to illustrate how erroneous methodologies have crept into the impact assessment literature. Several interesting cases of “ripple effects” including those related to the siting of hazardous waste disposal facilities are considered using this identity.     

Integrated assessment modeling of global climate change: Transparent rational tool for policy making or opaque screen hiding value‐laden assumptions?

One of the principal tools used in the integrated assessment (IA) of environmental science, technology and policy problems is integrated assessment models (IAMs). These models are often comprised of many sub‐models adopted from a wide range of disciplines. A multi‐disciplinary tool kit is presented, from which three decades of IA of global climatic change issues have tapped. A distinction between multi‐ and inter‐disciplinarity is suggested, hinging on the synergistic value added for the latter. Then, a hierarchy of five generations of IAMs are proposed, roughly paralleling the development of IAMs as they incorporated more components of the coupled physical, biological and social scientific disciplines needed to address a “real world” problem like climatic change impacts and policy responses. The need for validation protocols and exploration of predictability limits is also emphasized. The critical importance of making value‐laden assumptions highly transparent in both natural and social scientific components of IAMs is stressed, and it is suggested that incorporating decision‐makers and other citizens into the early design of IAMs can help with this process. The latter could also help IA modelers to offer a large range of value‐containing options via menu driven designs. Examples of specific topics which are often not well understood by potential users of IAMs are briefly surveyed, and it is argued that if the assumptions and values embedded in such topics are not made explicit to users, then IAMs, rather than helping to provide us with refined insights, could well hide value‐laden assumptions or conditions. In particular, issues of induced technological change, timing of carbon abatement, transients, surprises, adaptation, subjective probability assessment and the use of contemporary spatial variations as a substitute for time evolving changes (what I label “ergodic economics”) are given as examples of problematic issues that IA modelers need to explicitly address and make transparent if IAMs are to enlighten more than they conceal. A checklist of six practices which might help to increase transparency of IAMs is offered in the conclusions. Incorporation of decision‐makers into all stages of development and use of IAMs is re‐emphasized as one safeguard against misunderstanding or misrepresentation of IAM results by lay audiences.     

Deriving indicators of sustainable development

Assessments of current and future development paths require comprehensive sets of indicators covering all essential aspects. From a holistic systems point of view, most currently proposed indicator sets are incomplete and deficient. Assessments of sustainable development require a full representation of the satisfaction states of the “basic orientors” (=fundamental interests) of constituent sector systems, and of their contribution to basic orientor satisfaction of the total system. Basic orientors are value dimensions (existence, effectiveness, freedom of action, security, adaptability, coexistence) emerging from a self-organizing system's interaction with its environment, and its fundamental properties (normal environmental state, scarce resources, variety, variability, change, other systems). Basic orientors are also reflected in human emotions, societal punishment, psychological and social needs, life styles, and values emerging in self-organization of artificial life. The relative weight assigned to basic orientors of partner systems is a question of ethics. Based on these concepts, a general scheme for finding a “complete” set of indicators of viability and sustainability is derived, and it is applied to deriving a comprehensive set of indicators of sustainable development for society and its sector systems (infrastructure, economic system, social system, individual development, government, environment and resource system). The method can be applied at different levels of complexity and regional resolution. A full set of regional indicators for sustainable development is presented, a method for compact assessment of development paths using orientor stars is described, and the approach for application at the community level is outlined.     

The “START” framework to evaluate national progress in green buildings and its application in cases of Singapore and China

Current studies provide little knowledge for a holistic assessment of green building (GB) studies and practices. This study addresses the gap by developing a systematic and multidisciplinary framework called “START” that integrates five interactive dimensions of GB, including S-stakeholders, To-total building life cycle concerns, A-actions of government, R-relevant contexts, and Ta-targets to facilitate scholars and policymakers' analysis of GB development and progress. A comparative review of Singapore's and China's GB journey has been conducted to illustrate the application of the framework. Results indicate that both countries face common challenges of achieving economic sustainability of GB due to market failure and lack of non-technical support. However, Singapore has made better progress in environmental and social sustainability than China. Singapore's GB program inclusively covers the whole building life phases whereas China mainly emphasizes on planning/design phase. Collectively, effective governance, strict law enforcement, engaged stakeholders, matured economy and emerging technologies facilitate the GB implementing in Singapore. Additional efforts are required for China to cope with its complexity and heterogeneity regarding climate zones, hierarchy of authority, and disparity of economy and resources allocation in various regions. The framework helps facilitate policymakers to comprehensively assess GB progress and highlights the importance of incorporating non-technical drivers, such as social, psychological, political factors, into multi-disciplinary research of GB programs.     

Application of data envelopment analysis in environmental impact assessment of a coal washing plant: A new sustainable approach

The main problem of traditional methods of environmental impact assessment (EIA) is that in most of the existing algorithms and methods, such as Leopold, Folchi and RIAM, the main attention is to the destructive effects of the proposed plan, and the advantages of the industrial project are less noticeable. This has led to a permanent challenge between environmental organizations and industrial stakeholders. Data envelopment analysis (DEA) is a new approach of assessing the industrial units. Besides, it considers the positive economic and social impacts of the project and provides a comprehensive assessment of the industrial unit. With this approach, the environmental impacts of an industrial unit have been considered as “inputs” and its positive economic and social impacts considered as the “outputs” of the DEA models. Therefore, the problem of impact assessment changes into a DEA model. In the present study, the Alborz Sharghi Coal washing plant in northern Iran has been considered as a case study for implementing the DEA-EIA approach, and 19 plant activities and 11 environmental components have been used to evaluate the environmental effects of the plant. To solve the EIA problem, two commonly used DEA approaches, called CRS (constant returns to scale) and VRS (variable returns to scale), have been used. The DEA results identified the critical environmental components of the plant that should be considered seriously. Also, drawing the “potential improvement” diagram in the DEA method is an effective tool for determining the high risk activities of the factory and applying them in development plans. Besides, using the VRS model with maximize-output approach showed that some of the plant activities had the most differences with optimal mode and these components should be considered in future development plans. Finally, it can be concluded that, assessing the environmental impacts of the mineral industries with VRS maximize-output approach, is closer to the concept of sustainable development and cost-benefit analysis.     

地理3G系统应用于城市固体废物管理的新模式

基于GPS、GPRS、GIS地理3G综合技术,设计城市固体废物综合监管系统,实现对固体废物从收集到再利用全过程的监督管理。该系统由申报信息系统、地面控制系统、处置保障系统、综合分析系统、应急指挥系统和线上“淘宝”系统组成,在固体废物收集站点采集的数据上传至综合分析系统,经分析与匹配后相关信息发布在线上“淘宝”交易平台,平台为买卖双方提供资源配置方案,也为管理部门的宏观调控提供数据支撑。     

Sustainable building assessment tool for project decision makers and its development process

The primary purpose of Sustainable Building Assessment (SBA) tools is for behavioral changes in public building practice. In the Web 3.0 era, even non-experts may have the capacity to select and use Information & Communication Technology (ICT) tools to solve complicated problems in sustainable building decision making without any expert help. It is clear that the R&D of these tools which directly target project decision makers is a significant project for researchers and policy makers who are tasked with the challenge of changing individual building practice for regional sustainability. The purpose of the study is to suggest a tool and its development process for sustainable building assessment by project decision makers, especially targeting non-expert groups. This study defines an SBA tool for project decision makers based on a typology and suggests a “3-layer development process framework”. For the theoretical Background, we integrate interdisciplinary methods and principles such as cognitive problem-solving and sustainable building delivery, including long-term community benefits in a region and an information system development process. This framework is based on Boehem's spiral model for information system development processes and especially emphasizes the first cycle to develop a core set of indicators fit for the non-expert user's problem solving process. This presents an iterative and gradual process with the aim of approaching the tool type from passive tools such as checklists and guidelines, to interactive software, especially interactive DSS (Decision Support Systems) online ICT platforms. The development process is comprised of 8 stages: This includes the use of mixed-method sequential design, including interviews, workshops, the Delphi survey technique, FD-AHP weight analysis, and scenario analysis. In addition, the study presents a case study of the application of the first cycle to develop a core set of indicators and discusses its effectiveness. This study may help researchers by providing a clear and effective method to understand interdisciplinary approaches to develop appropriate tools for sustainable building practice in a regional context.     

南京市在长江大保护工作中推进生态环境大数据融合的思考

以习近平长江经济带发展思路和实施国家大数据战略为背景,简要介绍了南京市长江大保护和大数据融合工作的现状与特点。在对标分析武汉市长江大保护和贵州省大数据融合先进工作经验的基础上,针对当前南京市大数据应用水平不高,难以为生态环境重要决策提供有效支撑的情况,从树立创新思维摸清沿江污染底数、树立法治思维推进大数据融合共享、树立融入思维推进社会化服务、树立数字思维适应信息化和“互联网+”发展等方面,对南京市如何深度整合利用现有大数据资源,构建具有南京特色的长江大保护“生态眼”监管系统提出对策和建议。     

Cumulative effects of developed road network on woodland—a landscape approach

Population growth, during the twentieth century, has increased demand for new farmlands. Accordingly, road networks have rapidly been developed to facilitate and accelerate human access to the essential resources resulted in extensive land use changes. The present study aims at assessing cumulative effects of developed road network on tree cover of Golestan Province in northern Iran. In order to detect changes over the study period of 1987–2002, the LULC map of the study area was initially prepared from the satellite images of Landsat TM (1987) and ETM+ (2002) using maximum likelihood supervised classification method. Afterwards, a total number of seven landscape matrices were selected to detect cumulative effects of the developed road network on woodland cover. The obtained results indicated that the fragile patches are mainly located at a distance of 171–342 m from the roadside. Furthermore, the majority of the patches affected by cumulative effects of development activities are situated at a distance of 342–684 m from the roadside, over an approximate area of 55 ha. The analysis of landscape metrics revealed that the developed road network has increased the landscape metrics of “the number of patches” and “patches perimeter-area ratio”. It has also followed by a decrease in metrics such as “patches area”, “Euclidean nearest neighbor distance”, “patches proximity”, “shape index”, “contiguity”, and “mean patches fractal dimension”. The road network has also increased the “number of patches” and decreased the “mean patches area” representing further fragmentation of the landscape. With identification of highly affected wooldland cover patches, it would be possible to apply adaptive environmental management strategies to preserve and rehabilitate high-priority patches.     

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.     

Comparative life cycle assessments: The case of paper and digital media

The consumption of the written word is changing, as media transitions from paper products to digital alternatives. We reviewed the life cycle assessment (LCA) research literature that compared the environmental footprint of digital and paper media. To validate the role of context in influencing LCA results, we assessed LCAs that did not compare paper and print, but focused on a product or component that is part of the Information and Communication Technology (ICT) sector. Using a framework that identifies problems in LCA conduct, we assessed whether the comparative LCAs were accurate expressions of the environmental footprints of paper and print. We hypothesized that the differences between the product systems that produce paper and digital media weaken LCA's ability to compare environmental footprints. We also hypothesized that the characteristics of ICT as an industrial sector weaken LCA as an environmental assessment methodology. We found that existing comparative LCAs offered problematic comparisons of paper and digital media for two reasons — the stark material differences between ICT products and paper products, and the unique characteristics of the ICT sector. We suggested that the context of the ICT sector, best captured by the concept of “Moore's Law”, will continuously impede the ability of the LCA methodology to measure ICT products.     

A historical review of the cumulative science in SEA effectiveness

Based on research on the effectiveness of Strategic Environmental Assessment (SEA) and the critical factors determining it, numerous questions regarding SEA and its effectiveness have come to light. In this paper, we address a range of these questions based on a review of 39 peer-reviewed scientific articles. Questions are examined such as how effectiveness is defined and how the critical factors affecting it are determined. The concept of effectiveness is broadly accepted, but its meaning differs between different schools of thought. Effectiveness is clearly kept apart from the concept of quality, but in many definitions of SEA effectiveness, we have seen the concepts presented as overlapping and developing through time. At any point in time, a multitude of meanings of effectiveness thus exist, sustaining the idea of Thomas Kuhn that this kind of social science is not really cumulative. To overcome these limitations, we present a view of how these different concepts can be presented in a number of logical and historical steps. Based on the critical factors and their concentration into broader concepts, it targets to the formulation of a general, more comprehensive model that can describe the interrelationship between general- and stage-specific critical factors. It is concluded that “comprehensive models of causation” exist which condense a broader range of critical factors into only 4–10 main topics that describe the most pivotal forces at play. It may be possible to facilitate a common understanding of the nature of SEA and thus invite future research.     

A framework for adaptive monitoring of the cumulative effects of human footprint on biodiversity

Effective ecological monitoring is imperative in a human-dominated world, as our ability to manage functioning ecosystems will depend on understanding biodiversity responses to anthropogenic impacts. Yet, most monitoring efforts have either been narrowly focused on particular sites, species and stressors — thus inadequately considering the cumulative effects of multiple, interacting impacts at scales of management relevance — or too unfocused to provide specific guidance. We propose a cumulative effects monitoring framework that integrates multi-scaled surveillance of trends in biodiversity and land cover with targeted evaluation of hypothesized drivers of change. The framework is grounded in a flexible conceptual model and uses monitoring to generate and test empirical models that relate the status of diverse taxonomic groups to the nature and extent of human “footprint” and other landscape attributes. An adaptive cycle of standardized sampling, model development, and model evaluation provides a means to learn about the system and guide management. Additional benefits of the framework include standardized data on status and trend for a wide variety of biodiversity elements, spatially explicit models for regional planning and scenario evaluation, and identification of knowledge gaps for complementary research. We describe efforts to implement the framework in Alberta, Canada, through the Alberta Biodiversity Monitoring Institute, and identify key challenges to be addressed.