基于区域梯度式学习情景的长江经济带环境治理研究

长江经济带作为国家重大战略,面临开发与保护的巨大挑战,构建科学、高效的生态环境保护机制为其保驾护航成为重中之重。作为一个流域式生态共同体,区域统筹协调下的生态环境保护和治理是根本关键。本研究导入区域协同治理理念,提出构建梯度式渐进学习环境协同治理框架,并采用数据进行长江经济带环境污染治理的多情景测度与评价实证检验了梯度间以及梯度内部的环境保护与污染治理协调机制的潜在影响。最终,从中央、区域和地方三个层面提出梯度式学习视野下长江经济带环境治理策略。     

Transforming conservation science and practice for a postnormal world

We examine issues to consider when reframing conservation science and practice in the context of global change. New framings of the links between ecosystems and society are emerging that are changing peoples’ values and expectations of nature, resulting in plural perspectives on conservation. Reframing conservation for global change can thus be regarded as a stage in the evolving relationship between people and nature rather than some recent trend. New models of how conservation links with transformative adaptation include how decision contexts for conservation can be reframed and integrated with an adaptation pathways approach to create new options for global‐change‐ready conservation. New relationships for conservation science and governance include coproduction of knowledge that supports social learning. New processes for implementing adaptation for conservation outcomes include deliberate practices used to develop new strategies, shift world views, work with conflict, address power and intergenerational equity in decisions, and build consciousness and creativity that empower agents to act. We argue that reframing conservation for global change requires scientists and practitioners to implement approaches unconstrained by discipline and sectoral boundaries, geopolitical polarities, or technical problematization. We consider a stronger focus on inclusive creation of knowledge and the interaction of this knowledge with societal values and rules is likely to result in conservation science and practice that meets the challenges of a postnormal world.     

Evaluating Children's Conservation Biology Learning at the Zoo

Millions of children visit zoos every year with parents or schools to encounter wildlife firsthand. Public conservation education is a requirement for membership in professional zoo associations. However, in recent years zoos have been criticized for failing to educate the public on conservation issues and related biological concepts, such as animal adaptation to habitats. I used matched pre‐ and postvisit mixed methods questionnaires to investigate the educational value of zoo visits for children aged 7–15 years. The questionnaires gathered qualitative data from these individuals, including zoo‐related thoughts and an annotated drawing of a habitat. A content analysis of these qualitative data produced the quantitative data reported in this article. I evaluated the relative learning outcomes of educator‐guided and unguided zoo visits at London Zoo, both in terms of learning about conservation biology (measured by annotated drawings) and changing attitudes toward wildlife conservation (measured using thought‐listing data). Forty‐one percent of educator‐guided visits and 34% of unguided visits resulted in conservation biology‐related learning. Negative changes in children's understanding of animals and their habitats were more prevalent in unguided zoo visits. Overall, my results show the potential educational value of visiting zoos for children. However, they also suggest that zoos’ standard unguided interpretive materials are insufficient for achieving the best outcomes for visiting children. These results support a theoretical model of conservation biology learning that frames conservation educators as toolmakers who develop conceptual resources to enhance children's understanding of science. Evaluación del Aprendizaje de Biología de la Conservación por Niños en el Zoológico Jensen     

基于LLE-ELM的矿井瓦斯涌出量预测方法研究

基于局部线性嵌入(Locally Linear Embedding,LLE)算法和极限学习机(Extreme Learning Machine,ELM)神经网络建立矿井瓦斯涌出量预测模型,该预测模型运用LLE算法对矿井瓦斯涌出量影响因素样本进行数据挖掘,得到降维后的有效因子,再将这些有效因子作为ELM神经网络的输入层进行训练和预测。利用某矿井的实测数据进行实例分析,结果表明该预测模型预测速度快,精度高,能够用于矿井瓦斯涌出量预测。     

Deciphering urban traffic impacts on air quality by deep learning and emission inventory

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM_2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 µg/m³ to 12.283 µg/m³ compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM_2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction. We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_2.5 about 8.45% by 2030 gradually. These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control, and eventually benefit people's lives and high-quality sustainable developments of cities.     

Institutionalising social learning: Towards systemic and adaptive governance

This paper critically examines how public policy makers limit policy and other institutional design choices by a failure to appreciate (i) how situations may be characterised or framed; (ii) how practices that generate neologisms (invented terms or concepts) or reify (make into a thing) abstract concepts can displace understandings, and (iii) the epistemological bases of governance mechanism choices. An inquiry into the coining of the neologisms ‘wicked’ and ‘tame’ problems is reported and the implications for research and policy practice explored. As practices, neologising, reifying, categorising and typologising have unintended consequences – they remove us from the primary experiences and underlying emotions that provided the motivation for formulating these concepts in the first place. The failure to institutionalise the understandings and experiences that sit behind the invention of the terms ‘wicked’ and ‘tame’ problems (or similar framing choices such as ‘problematique’, ‘messes’, ‘lowland real-life swamps’, ‘resource dilemmas’ or ‘complex adaptive systems’) present systemic constraints to institutionalising social learning as an alternative yet complementary governance mechanism within an overall systemic and adaptive governance framework. Ultimately situations usefully framed as ‘wicked’,’ such as water managing and climate change are problems of relationship – of human beings with the biosphere. Re-framings, such as institutions as social technologies and other research and praxis traditions concerned with the breakdown of relationships may offer ways forward in the purposeful designing and crafting of more effective institutions.     

Marshaling Adaptive Capacities within an Adaptive Management Framework to Enhance the Resiliency of Wastewater Systems

We assess adaptive capacity and adaptive management as measures of wastewater (WW) system resiliency using data from interviews with WW system managers (hereafter managers) impacted by past storms. Results suggest the most resilient WW systems are those with high adaptive capacities that employ an adaptive management approach to make ongoing adaptation investments over time. Greater amounts of generic adaptive capacities (i.e., skilled staff and good leadership) help smooth both day‐to‐day and emergency operations and provide a foundation for adaptive management. In turn, adaptive management helps managers both build more generic adaptive capacities, and develop and employ greater amounts and diversity of specific adaptive capacities (i.e., soft and/or hard adaptations) that are especially important for enhancing and sustaining resiliency. Adaptive management also enables managers to better understand their system's vulnerabilities, how those vulnerabilities change over time, and what specific actions may reduce those vulnerabilities. Finally, our work suggests WW system resilience critically depends on the capacities of the human systems for building resilience as much as or more so than relying only on physical infrastructure resilience. Our work contributes to filling an important gap in the literature by advancing our understanding of the human dimensions of infrastructure resilience and has practical implications for advancing resilience in the WW sector.     

Applying deep learning to right whale photo identification

Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time-consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport-like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities.     

Using deep-belief networks to understand propensity for livelihood change in a rural coastal community to further conservation

Overharvesting of terrestrial and marine resources may be alleviated by encouraging an alternative configuration of livelihoods, particularly in rural communities in developing countries. Typical occupations in such areas include fishing and farming, and rural households often switch livelihood activities to suit climate and economic conditions. We used a machine-learning tool, deep-belief networks (DBN), and data from surveys of a rural Philippine coastal community to examine household desire to change livelihood. This desire is affected by a variety of factors, such as income, family needs, and feelings of work satisfaction, that are interrelated in complex ways. In farming households, livelihood changes often occur to diversify resources, increase income, and lessen economic risk. The DBN, given its multilayer perceptron structure, has a capacity to model nonlinear relationships among factors while providing an acceptable degree of accuracy. Relative to a set of 34 features (e.g., education, boat ownership, and work satisfaction), we examined the binary response variables desire to change work or not to change work. The best network had a test set accuracy of 97.5%. Among the features, 7 significantly affected desire to shift work: ethnicity, work satisfaction, number of persons in a household in ill health, number of fighting cocks owned, fishing engagement, buy-and-sell revenue, and educational level. A cross-correlation matrix of these 7 features indicated households less inclined to change work were those engaged in fishing and retail buying and selling. For fishing, provision of economic and other incentives should be considered to encourage changing from this occupation to allow recovery of fishery resources.