欧盟碳价影响因素研究及其对中国的启示

作为一项市场创新和政策创新,即将启动的中国全国性碳市场备受国内外关注。为保证其成功建立与平稳发展,相关经验借鉴已刻不容缓,但作为投石问路的7大试点碳市场发展层次不齐,可供参考的模式有限,因此研究全球第一大碳市场——欧盟碳排放交易体系(EU ETS)及其对中国的可参照性尤为迫切;而作为市场是否成熟的风向标,碳价规律性特征的挖掘尤为重要。前期国内外学者分别发现CER价格、原油价格、煤炭价格、天然气价格、欧洲工业指数、联合国气候变化大会、政府政策、极寒天气、暖冬天气、自然灾害、重大事件等多种因素都有可能引起EUA期货价格波动。本研究通过引入MIV-BP神经网络模型,对EU ETS二期和三期的EUA期货价格进行训练和测试,模拟了上述11个因素对EUA价格的影响,弥补了传统计量模型难以同时处理较多变量及不能整合定性与定量变量等缺点。通过对EU ETS二期1 149组和三期775组数据的挖掘,得出了各变量对EUA期货价格的影响程度。其中,二期运行阶段各变量影响程度从大到小排序为:自然灾害COPCER极寒天气Coal重大事件Brent政府政策Stock600Gas暖冬天气;三期运行阶段各变量影响程度从大到小排序为:COPStock600Coal自然灾害极寒天气重大事件政府政策BrentGasCER暖冬天气。最后,本研究对二、三期各变量对碳价影响程度的变化进行了解释,并对中国未来建立全国性碳市场提出了以下四点建议:(1)稳定碳市场参与主体预期;(2)完善核证减排抵消机制,保持政策稳定;(3)配额分配考虑区域差异;(4)建立配额应急机制。     

Scale Mismatches,Conservation Planning,and the Value of Social‐Network Analyses

Many of the challenges conservation professionals face can be framed as scale mismatches. The problem of scale mismatch occurs when the planning for and implementation of conservation actions is at a scale that does not reflect the scale of the conservation problem. The challenges in conservation planning related to scale mismatch include ecosystem or ecological process transcendence of governance boundaries; limited availability of fine‐resolution data; lack of operational capacity for implementation; lack of understanding of social‐ecological system components; threats to ecological diversity that operate at diverse spatial and temporal scales; mismatch between funding and the long‐term nature of ecological processes; rate of action implementation that does not reflect the rate of change of the ecological system; lack of appropriate indicators for monitoring activities; and occurrence of ecological change at scales smaller or larger than the scale of implementation or monitoring. Not recognizing and accounting for these challenges when planning for conservation can result in actions that do not address the multiscale nature of conservation problems and that do not achieve conservation objectives. Social networks link organizations and individuals across space and time and determine the scale of conservation actions; thus, an understanding of the social networks associated with conservation planning will help determine the potential for implementing conservation actions at the required scales. Social‐network analyses can be used to explore whether these networks constrain or enable key social processes and how multiple scales of action are linked. Results of network analyses can be used to mitigate scale mismatches in assessing, planning, implementing, and monitoring conservation projects. Discordancia de Escalas, Planificación de la Conservación y el Valor del Análisis de Redes Sociales     

基于无线局域网(WLAN)的油田安全监控系统研究

针对油田监控对象具有点多、面广、跨区域的特点,指出对油田进行无线监控的必要性,并对6种常见的无线通信手段的特点进行比较;根据油田安全监控的业务需求,提出基于WLAN技术的油田安全监控系统硬件结构和软件功能设计方案;并结合工程实施案例介绍系统中无线设备的选型、安装和防雷接地等方面的工程问题。该研究成果实现油田的安全监控预警,减少或避免事故发生的可能性,有利于提高油田安全管理的科学性,具有实用推广价值。     

文化和旅游的关系网络及其融合路径研究

为破解文化和旅游融合发展过程中的困难和挑战,借鉴共生理论、社会互动理论和互惠规范理论,构建了文化和旅游"共生—共建—共享"的关系网络,并进一步从政府、企业和旅游者角度提出文旅融合发展路径,最终构建了文化和旅游在理念、职能、产业、市场、服务和交流方面的六大融合矩阵。     

自然环境因素对铝合金大气腐蚀的影响性研究

目的 研究铝合金大气腐蚀受自然环境因素的影响性，探索环境因素对铝合金腐蚀的影响规律。方法 采用多层感知器神经网络分析环境因素对7种铝合金平均腐蚀速率的影响。结果 确定了影响铝合金腐蚀损伤的重要因素为SO2沉积率、Cl^–沉积率、NOx沉积率；辅要因素为年降雨量、年雾露时数、相对湿度、年日照时数、温度、铝合金成分；次要因素为年累积降水时数、雨水pH值。结论 基于多层感知器神经网络分析有效展现了环境因素对平均腐蚀速率的影响性。     

Institutional networks and adaptive water governance in the Klamath River Basin,USA

Polycentric networks of formal organizations and informal stakeholder groups, as opposed to centralized institutional hierarchies, can be critically important for strengthening the capacity of governance systems to adapt to unexpected social and biophysical change. Adaptive governance is one type of environmental governance characterized by the emergence of networks that stimulate adaptive capacity through increases in social-learning, communication, trust, public participation and adaptive management. However, detecting and analyzing adaptive governance networks remains elusive, especially given contexts of highly contested resource governance such as large-scale negotiations over water use. Research methods such as social network analysis (SNA) are often infeasible as they necessitate collecting in-depth and politically sensitive personal data from a near-complete set of actors or organizations in a network. Here we present a method for resolving this problem by describing the results of an institutional SNA aimed at characterizing the changing governance network in the Klamath River Basin, USA during a period of contested negotiations over water. Through this research, we forward a method of institutional SNA useful when an individual or egocentric approach to SNA is problematic for political, logistical or financial reasons. We focus our analysis on publically available data signaling changes in formal relationships (statutory, regulatory, contractual) between organizations and stakeholder groups. We find that employing this type of SNA is useful for describing potential and actual transitions in governance that yield increases in adaptive capacity to respond to social and biophysical surprises such as increasing water scarcity and changes in water distribution.     

Bird‐community responses to habitat creation in a long‐term,large‐scale natural experiment

Ecosystem function and resilience are compromised when habitats become fragmented due to land‐use change. This has led to national and international conservation strategies aimed at restoring habitat extent and improving functional connectivity (i.e., maintaining dispersal processes). However, biodiversity responses to landscape‐scale habitat creation and the relative importance of spatial and temporal scales are poorly understood, and there is disagreement over which conservation strategies should be prioritized. We used 160 years of historic post‐agricultural woodland creation as a natural experiment to evaluate biodiversity responses to habitat creation in a landscape context. Birds were surveyed in 101 secondary, broadleaf woodlands aged 10–160 years with ≥80% canopy cover and in landscapes with 0‐17% broadleaf woodland cover within 3000 m. We used piecewise structural equation modeling to examine the direct and indirect relationships between bird abundance and diversity, ecological continuity, patch characteristics, and landscape structure and quantified the relative conservation value of local and landscape scales for bird communities. Ecological continuity indirectly affected overall bird abundance and species richness through its effects on stand structure, but had a weaker influence (effect size near 0) on the abundance and diversity of species most closely associated with woodland habitats. This was probably because woodlands were rapidly colonized by woodland generalists in ≤10 years (minimum patch age) but were on average too young (median 50 years) to be colonized by woodland specialists. Local patch characteristics were relatively more important than landscape characteristics for bird communities. Based on our results, biodiversity responses to habitat creation depended on local‐ and landscape‐scale factors that interacted across time and space. We suggest that there is a need for further studies that focus on habitat creation in a landscape context and that knowledge gained from studies of habitat fragmentation and loss should be used to inform habitat creation with caution because the outcomes are not necessarily reciprocal.     

Evaluating outcomes of conservation with multidimensional indicators of well-being

Many conservation interventions are hypothesized to be beneficial for both the environment and people's well-being, but this has rarely been tested rigorously. We examined the effects of adoption or nonadoption of a conservation intervention on 3 dimensions of people's well-being (material, relational, and subjective) over time. We focused on a fisheries bycatch management initiative intended to reduce environmental externalities associated with resource extraction. We collected panel data from fishers (n = 250) in villages with (adopters and nonadopters) and without (control) the conservation intervention 3 times over 2 years. We found no evidence that adoption reduced any of the 3 dimensions of well-being in the local populations affected by the intervention. There were modest improvements in material (t = –1.58) and subjective livelihood well-being (p = 0.04) for adopters relative to nonadopters over time. The variations in well-being experiences (in terms of magnitude of change) among adopters, nonadopters, and controls across the different domains over time affirmed the dynamic and social nature of well-being.     

Pulling force prediction using neural networks

Purpose. In ergonomics and human factors investigations, pulling force (PF) estimation has usually been achieved using various types of biomechanical models, and independent approximation of PF was done with the help of upper extremity joints. Recently, multiple regression methods have gained importance for task-relevant inputs in predicting PF. Artificial neural networks (ANNs) also play a vital role in fitting the data; however, their use in work-related biomechanics and ergonomics is inadequate. Therefore, the current research aimed to accomplish comparative investigation of ANN and regression models by assessing their capacity to predict PF values. Methods. Multipositional PF data were acquired from 200 subjects at three different handle heights and body locations. ANN and regression models were formed using a random sample of three subsets (75% training, 15% selection, 10% validation) for proving the outcomes. Results. The comparison of ANN and regression models shows that the predictions of ANN models had a profoundly explained variance and lower root mean square difference values for the PF data at three handle heights. Conclusions. These outcomes advise that ANNs offer a precise and robust substitute for regression methods, and should be considered a useful method in biomechanics and ergonomics task assessments.     

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation = 3.2, validation r² = 0.91 and RMSE = 13.38 μg g^?1 h^?1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.