Collective action in water-point management: The case of pastoral and agropastoral communities in eastern Ethiopia

This paper explores collective activities in water-point management by pastoral and agropastoral herders. It examines conditions that strengthen or weaken cooperative behaviour. By employing a comparative case study approach, the paper covers three types of water-points: ponds, cisterns and water wells. Results show that factors beyond the expectation of economic benefits influence the development of cooperative behaviour, including embedded social relationships, which provide a basis for sanctions. However, the absence of clear allocation rules and of secure property rights discourages sustained contribution of inputs. The study also finds that the contribution, and the ultimate benefits gained, by the members of the group are dependent on limited technological options and other attributes of the water-points they manage. Interestingly, divergence of interests (between economic elites and poor herders) in institutional change precluding the establishment of cisterns negatively affects the livelihoods of the poor (agro) pastoralists. Where internal capacity is limited, assisted collective action that builds on diverse local interests could contribute to sustainable management of collectively used water-points. The study concludes that both economic incentives and socio-cultural norms affect collective action in a more complex way than the theory of determinants of collective action predicts.     

Assessing the role of learning devices and geovisualisation tools for collective action in natural resource management: Experiences from Vietnam

In northern Vietnam uplands the successive policy reforms that accompanied agricultural decollectivisation triggered very rapid changes in land use in the 1990s. From a centralized system of natural resource management, a multitude of individual strategies emerged which contributed to new production interactions among farming households, changes in landscape structures, and conflicting strategies among local stakeholders. Within this context of agrarian transition, learning devices can help local communities to collectively design their own course of action towards sustainable natural resource management. This paper presents a collaborative approach combining a number of participatory methods and geovisualisation tools (e.g., spatially explicit multi-agent models and role-playing games) with the shared goal to analyse and represent the interactions between: (i) decision-making processes by individual farmers based on the resource profiles of their farms; (ii) the institutions which regulate resource access and usage; and (iii) the biophysical and socioeconomic environment. This methodological pathway is illustrated by a case study in Bac Kan Province where it successfully led to a communication platform on natural resource management. In a context of rapid socioeconomic changes, learning devices and geovisualisation tools helped embed the participatory approach within a process of community development. The combination of different tools, each with its own advantages and constraints, proved highly relevant for supporting collective natural resource management.     

第四次工业革命对环境治理体系建设的影响与挑战

以人工智能为代表的第四次工业革命发展迅速,特别是能源和新材料技术的突破,极大可能改变人类现有能源格局,并对建立在化石能源思维下的传统环境治理体系带来巨大影响。为此,本文梳理了第四次工业革命代表性的科技进展,分析了其对国家环境治理体系的积极影响:(1)将改变建立在化石能源基础上的工业、农业和城市形态,打破经济发展对资源、能源和土地的高度依赖,彻底告别环境污染和生态破坏;(2)将真正实现面向现在和未来的数据精准决策,显著改善人类应对不确定性环境风险能力。另一方面,由于人工智能会极大提升公民个体对复杂科学技术和社会规则的掌握能力,会改变传统治理结构中政府—市场—社会三元力量对比,给政府在环境治理领域的政策制定和执行带来新的挑战:(1)公众的能力足以使得他们对政府做出的任何不利于自己的政策提出挑战,政府信息公开和透明决策压力空前;(2)社交媒体使得公众对政府及主流媒体信息的关注大大下降,对政府如何利用新型社交媒体进行环境保护宣传教育提出了新的挑战,同时也对社会管理部门如何培育环保社会组织,弥补政府影响力下降所产生的空白提出了新的课题;(3)个体可以同时具有律师、会计、记者等处理复杂社会问题的能力,挑战政府维护社会公平稳定的利益裁量权,对环境司法公正提出更高要求。当前,中国正在进行环境体制改革创新,追求环境治理体系和治理能力现代化,如何关注、分析并积极适应由于技术变革引起的经济基础和上层建筑变化,避免制度资源错配,对于我们这样一个正在进行工业化的后发型国家尤为重要。文章最后建议,要高度关注这个领域的研究前沿进展,在制定发展规划与改革措施过程中,以未来的技术环境为出发点;要加强治理创新,引导技术朝着有利环境保护的方向发展;要加强技术对国家治理体系和治理能力双向影响研究,推进政府改革与创新,为人民提供更好的公共产品。     

Prediction of primary energy demand in China based on AGAEDE optimal model

In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the model(both linear and quadratic) are optimized by AGA using factors,such as GDP,population,urbanization rate,and RD inputs together with energy consumption structure,that affect demand.Since the spurious regression phenomenon occurs for a wide range of time series analysis in econometrics,we also discuss this problem for the current artificial intelligence model.The simulation results show that the proposed model is more accurate and reliable compared with other existing methods and the China's energy demand will be 5.23 billion TCE in 2020 according to the average results of the AGAEDE optimal model.Further discussion illustrates that there will be great pressure for China to fulfill the planned goal of controlling energy demand set in the National Energy Demand Project(2014—2020).     

Fundamental insights on when social network data are most critical for conservation planning

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state-dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small-scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species-poor sites are subsets of species-rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small-scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un-nested and when social networks are likely to be centralized.     

A review on the integration of artificial intelligence into coastal modeling

With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.     

Anti-predatory vigilance and the limits to collective detection: visual and spatial separation between foragers

Collective detection concerns the idea that all members of a socially feeding group are alerted to an attack as long as at least one group member detects it. We found that collective detection in mixed flocks of emberizid sparrows is limited markedly by relatively small degrees of visual and spatial separation between foragers. These limits on collective detection appear to influence the degree to which flock members lower their vigilance with increasing group size (the group size effect). Specifically, the decrease in collective detection with increasing visual and spatial isolation between foragers is accompanied by a concomitant decrease in the strength of the vigilance group size effect. Explanations for the vigilance-related effects of such separation based upon a bird’s ability to monitor the vigilance behavior of flockmates can be ruled out for our experimental system. Our results also shed light on the issue of whether the vigilance group size effect is influenced more by collective detection or the simple dilution of risk with increasing group size. We argue that collective detection is not only an important determinant of the group size effect, but also that the phenomena of collective detection and risk dilution are interdependent. Received: 25 July 1995/Accepted after revision: 17 December 1995     

试论高校图书馆开展竞争情报服务与教育

社会对竞争情报人才和竞争情报的需求越来越迫切,各种形式的竞争情报教育与服务发展越来越迅速.目前高校图书馆由于竞争情报意识不强、用户对图书馆的信任程度不够、高校竞争情报工作处在起步阶段等诸多不利因素,因而要增强情报意识和竞争情报宣传与培训,提高竞争情报人员的综合素质.     

Irrigation and collective action: A study in method with reference to the Shiwalik Hills, Haryana

In recent years decentralized development approaches have gained prominence in the agricultural sector. A host of community based watershed management projects have been implemented that encourage community organizations to undertake management of previously government controlled irrigation systems and forests. Community organizations have been given the responsibility of managing water distribution, collection of irrigation service fees and undertaking routine maintenance of irrigation infrastructure. In this context, analysis of irrigation management has concluded that groups that are relatively homogeneous may fare better than heterogeneous groups in facilitating collective action. However, this article argues that analysis of the influence of group heterogeneity on collective action is complicated because of its multi‐dimensional nature and the presence of non‐monotonic effects in mechanisms linking heterogeneity and collective outcomes. The article discusses the importance of context specification in analysis of group heterogeneity through a discussion of elements of a joint management contract in Haryana (India), identification of key variables with a potential to explain collective action in irrigation management and construction of household endowment and water interest scores to account for the influence of group heterogeneity in facilitating collective action. In the process of applying household endowment and water interest scores, the authors highlight the role of local ecological variation and non‐farm employment in influencing collective action. Proper specification of local context enables the researchers to rely on household endowment and water interest scores to predict conflicts and potential for irrigation service provision and compliance with irrigation service rules.