The role of science-policy interface in sustainable urban water transitions: Lessons from Rotterdam

This paper investigates the role of the science-policy interface in leveraging transitions to sustainable urban water management. The paper presents a case study of the Dutch city of Rotterdam, which is increasingly regarded as a global leader in adaptive and resilient urban water management. The analysis reveals that Rotterdam’s transition has occurred incrementally over the past 15 years, driven by policy entrepreneurs: largely municipal policy makers and policy practitioners. Strategic use of the science-policy interface (SPI) has facilitated the development of innovative solutions to achieve policy goals and created the enabling conditions necessary for transformative change. The Rotterdam case suggests that an effective SPI requires: (1) compelling water narrative; (2) cross-sectoral collaboration; (3) co-production of knowledge; (4) experiential evidence-based learning; (5) strategic use of trusted scientists; (6) fostering networks; and (7) generating business from science-based innovation. Rotterdam’s strategic approach to knowledge and innovation coupled with a new narrative around water sets it apart from many other cities and adds a new dimension to debates regarding enabling factors for advancing sustainable practices. These findings will be of interest to those engaged in urban water management policy and practice, environmental governance, and debates over transitions more broadly.     

The impact of team familiarity and team leader experience on team coordination errors: A panel analysis of professional basketball teams

To explore the dynamics involved in team coordination, we examine the impact of team familiarity and team leader experience on team coordination errors (TCEs). We argue that team familiarity has a U‐shaped effect on TCEs. We study the moderating effects of team leader prior experience and team leader team‐specific experience on the association between team familiarity and TCEs. We use panel data on teams from the National Basketball Association to test the hypotheses. Our findings support the U‐shaped relationship between team familiarity and TCEs and the moderating effect of team leader team‐specific experience on this relationship. The paper advances research on errors in organizations by analyzing the antecedents of TCEs, so far an underexplored empirical phenomenon. Moreover, it contributes to research on coordination in teams by empirically examining the interplay between formal and informal coordination mechanisms. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive co-management and network learning in the Room for the River programme

Adaptive co-management and learning are paramount for integrated flood risk management. Relevant literature focuses on adaptation at the level of physical and societal systems. The level of projects and programmes is largely overlooked, but they comprise interventions that adapt our physical systems and they provide opportunities for learning to contribute to transitions of societal systems. This paper aims to increase understanding on how learning takes place and can be stimulated within a programme. The mixed-method case study of Room for the River, a €2.3 billion programme for flood risk management, shows that a programme can be organised using various governance arrangements to stimulate learning and be a means for adaptive co-management to deliver upon environmental objectives.     

Influencing diversity beliefs through a personal testimonial,promotion-focused approach

Although researchers have highlighted the importance of diversity beliefs (i.e., team members' perceived value of diversity) for the elaboration of information in teams, little attention has been paid to whether and how diversity beliefs can be shaped. Drawing on theory and research on team diversity beliefs, we propose that diversity beliefs are more effectively influenced by interventions using a promotion (compared with a prevention) focus toward diversity and personal testimonial (compared with factual) knowledge. Results from an experiment conducted with 175 teams revealed that both a promotion focus and personal testimonial knowledge independently contributed to more positive diversity beliefs and consequently increased team elaboration of task-relevant information as well as integration of different perspectives. Our results reveal key factors that can influence diversity beliefs and underscore the pivotal role of diversity beliefs in improving the extent to which team members elaborate information and integrate diverse perspectives.     

Machine learning based quantitative consequence prediction models for toxic dispersion casualty

Incidental release of toxic chemicals can pose extreme danger to life in the vicinity. Therefore, it is crucial for emergency responders, plant operators, and safety professionals to have a fast and accurate prediction to evaluate possible toxic dispersion life-threatening consequences. In this work, a toxic chemical dispersion casualty database that contains 450 leak scenarios of 18 toxic chemicals is constructed to develop a machine learning based quantitative property-consequence relationship (QPCR) model to estimate the affected area caused by toxic chemical release within a certain death rate. The results show that the developed QPCR model can predict the toxic dispersion casualty range with root mean square error of maximum distance, minimum distance, and maximum width less than 0.2, 0.4, and 0.3, which indicates that the constructed model has satisfying accuracy in predicting toxic dispersion ranges under different lethal consequences. The model can be further expanded to accommodate more toxic chemicals and leaking scenarios.     

基于无人机影像的城市植被精细分类

针对传统植被资源调查方法工作量大、成本高、效率低的问题,利用高分辨率无人机遥感影像,联合地物光谱-纹理-空间信息,构建了一种适用于描述城市不同植被种类的多维特征空间,在此基础上对三种应用广泛的分类算法(基于像素的、面向对象的支持向量机及深度学习Mobile-Unet语义分割模型)开展了对比分析研究.结果表明:本文提出的联合地物光谱-纹理-空间信息的特征空间构建方法能够有效地描述城市不同类型植被的特征差异,提升影像分割、植被分类的精度;在分类精度上,基于像素和面向对象的支持向量机分类结果的总体精度均超过90%,深度学习方法的总体分类精度为84%;在算法效率上,传统机器学习方法也优于深度学习方法.因此,得出结论针对城市小区域、小样本的植被精细分类,传统机器学习分类方法比深度学习方法效果更好.     

人为排放及气象因素对空气质量影响的定量分析——以疫情期间邢台市为例

选择河北省邢台市作为研究对象,将2020疫情作为一个极限管控措施下的极限减排实验情景,把2021疫情作为未来常态化疫情防控实验分析情景.与疫情前期对比,两次疫情期间臭氧浓度均有提升且2021疫情时期颗粒物浓度同样有提高,2020疫情时期其他污染物浓度均有不同程度的改善,而与2019历史同期相比,两次疫情期间臭氧浓度同样有升高现象,除此以外,2021疫情时期污染物改善较好.利用长短期记忆网络（LSTM）算法和空气质量预报模式系统（WRF-CMAQ）量化了两次疫情时期气象因素对于污染物浓度变化的影响,根据空气质量模拟法反推了不同污染物受人为影响的浓度变化.实验结果表明,LSTM算法在两次疫情期间的模拟均显示人为影响对污染物产生了负影响（降低了污染物浓度）且在总变化影响中占比较高,而CMAQ模式模拟结果中的气象因素影响占比远高于LSTM算法.CMAQ模式在两次疫情模拟中表现出了不同的结果,在2020疫情中人为影响占据了主导,而在2021疫情中,相比较2020疫情时期,除NO₂外,人类活动对其他污染物的影响均为正值（促进了污染物浓度升高）.     

Developing an interdisciplinary and cross‐sectoral community of practice in the domain of forests and livelihoods

Although significant resources are being spent researching and fostering the relationship between forests and livelihoods to promote mutually beneficial outcomes, critical gaps in understanding persist. A core reason for such gaps is that researchers, practitioners, and policy makers lack the structured space to interact and collaborate, which is essential for effective, interdisciplinary research, practice, and evaluation. Thus, scientific findings, policy recommendations, and measured outcomes have not always been synthesized into deep, systemic understanding; learning from practice and implementation does not easily find its way into scientific analyses, and science often fails to influence policy. Communities of practice (CofPs) are dynamic sociocultural systems that bring people together to share and create knowledge around a common topic of interest. They offer participants a space and structure within which to develop new, systemic approaches to multidimensional problems on a common theme. Uniquely informed by a systems‐thinking perspective and drawing from the scientific and gray literatures and in‐depth interviews with representatives of established CofPs in the natural resource management and development domain, we argue that a well‐designed and adequately funded CofP can facilitate interdisciplinary and cross‐sectoral relationships and knowledge exchange. Well‐designed CofPs integrate a set of core features and processes to enhance individual, collective, and domain outcomes; they set out an initial but evolving purpose, encourage diverse leadership, and promote collective‐identity development. Funding facilitates effective communication strategies (e.g., in person meetings). We urge our colleagues across sectors and disciplines to take advantage of CofPs to advance the domain of forests and livelihoods.     

基于村庄分散度的农村生活污水治理模式量化分析

随着农村地区生活水平的提升,生活污水所含污染物成分趋于复杂化,未经处理就地排放对生态环境影响不容小觑。目前,农村生活污水治理模式种类多样,但决策方法缺乏科学依据。以内蒙古地区82个村落为研究对象。以自然村落为最小研究单元,利用村落宅基地占比、村落长宽比、周长、极限距离、坡度0°~8°占比和坡度标准差等指标,构建了居住密度决策树 (准确率76%) 以及地势决策树 (准确率88%) 。决策树所得结果通过二分类概率单位回归 (Probit)分析 (准确率87%) ,确定其村落集中或分散情况,进而选择适宜的村落污水治理模式 (集中模式、纳管模式、分散模式、拉运模式、就地利用模式) 。农村生活污水治理模式决策方法的确立决策指标的量化,为促进农村生活污水治理体系的发展、提升污水治理的有效性具有积极意义。     

The role of social learning for soil conservation: the case of Amba Zuria land management,Ethiopia

Social learning plays key roles in sustainable natural resource management; however, studies on its role show mixed results. Even though most current studies highlight positive outcomes, there are also negative effects of social learning with respect to natural resource management. This paper explores the influence of social learning outcomes on the adoption of soil conservation practices in Amba Zuria, Ethiopia. Data were collected through semi-structured interviews, group discussions and in workshops. The interviews were transcribed and analysed with the qualitative data analysis software ATLAS.ti. In addition, a review of pertinent documents and literature complements the analysis. We found that social learning results in positive outcomes in interactions among the actors, broader understanding on soil conservation, trust and mutual understanding, and the creation of opportunities to apply both indigenous and scientific knowledge. These outcomes, in turn, foster the adoption of stone terraces to conserve soils. No such positive results were found for farmers who were not involved in the learning process and who did not adopt stone terraces. The non-adoption can partly be explained by their exclusion from sharing the positive outcomes of social learning. The study also shows limitations related to the short duration of the learning platforms, insufficient representation of women and irregular patterns of learning schedules. Hence, Ethiopian soil conservation strategies should put emphasis on social learning in order to strengthen the collective processes of soil conservation measures.