Social learning from public engagement: dreaming the impossible?

Learning that transcends participation processes is critical if public engagement is to translate into a legacy of enhanced environmental citizenship. However, a lack of empirical evidence has limited discussion to date to largely ‘aspirational’ claims. This paper offers the first rigorous examination of whether public participation does generate beyond-process social learning. Initially we review the literature on public participation and environmental citizenship to identify the key dimensions of social learning. We then re-visit a well-worked case study of an innovative public engagement process on the Hampshire waste strategy from the 1990s. Approximately one third of the original participants have been interviewed to identify whether and how the experience had a lasting effect on them. Key methodological difficulties are discussed, not least the analytical difficulties of attributing learning to a process that happened ten years previously. However, we argue that there is evidence that both instrumental and communicative learning have taken place, and conclude by identifying key areas that require further research.     

基于PCA-PNN的采空区多源指标危险性辨识

为了提高采空区多源指标危险性辨识的预测精度,基于主成分分析(PCA)和概率神经网络(PNN),提出1种采空区多源指标危险性辨识方法。将影响华东某地区矿山采空区危险性辨识的9项因素作为主要影响因素,并以96个实测采空区为例进行分级。研究结果表明：与朴素贝叶斯、随机森林和AdaBoost 3种机器学习算法相比,PNN在测试集上表现更好,对实际工程具有良好的指导意义和应用价值。     

煤电大气汞驱动因素、排放预测和减排策略研究:以辽宁省为例

煤电是我国汞污染的重要来源,旨在控制汞排放的《水俣公约》也将煤电列为重点管控源.因此,我国急需提出科学合理的策略以指导煤电汞减排工作.明确煤电汞排放的驱动因素以及预测未来趋势是制定污染控制政策的基础.鉴于此,本文以辽宁省为例,综合运用对数平均迪氏分解法、环境学习曲线和情景分析模型,辨识了燃煤电厂汞排放的主要影响因素,并预测了未来十年的大气汞排放量.结果显示:2006—2017年排放从5009kg(-57.8%,79.1%)大幅下降至1419kg(-61.1%,80.2%),排放因子、煤炭消耗效率和电力行业结构是汞排放的主要抑制因素,而电力需求拉动了辽宁省大气汞排放.在基准情景下,燃煤电厂大气汞排放从2017年的1419kg下降至2030年的1243kg.在环境规划和严格控制情景下,2030年燃煤电厂大气汞排放分别下降了1200kg和1274kg.最后,本文针对辽宁省汞减排提出一系列政策建议:①通过优化电厂污染物控制设施,提高洗煤比例等措施降低汞排放因子;②继续淘汰低效燃煤电厂,并推广节能技术以提高煤炭消耗效率;③推进风能、光能等可再生能源替代煤电.     

DEHP暴露对小鼠神经行为学及脑脂质过氧化物的影响

为研究增塑剂邻苯二甲酸二乙基己酯(Di-2-ethylhexyl phthalate,DEHP)对脑组织造成氧化损伤以及对小鼠神经行为(主要包括学习、记忆能力与情绪)的影响.将24只雄性昆明小鼠随机分成4组,每组6只,分别为对照组(生理盐水),低浓度染毒组(5mg·kg-1DEHP,L组),中浓度染毒组(50mg·kg-1DEHP,M组),高浓度染毒组(500mg·kg-1DEHP,H组).利用Y型电迷宫检验小鼠的学习记忆能力变化,利用悬尾实验和强迫游泳实验检测小鼠抑郁行为的变化.然后取出脑组织,检验活性氧簇(ROS)及丙二醛(MDA)含量.数据显示,染毒组小鼠的学习记忆及行为学控制能力明显低于对照组(p<0.05),并且随DEHP浓度的增大小鼠的学习记忆及行为学控制能力降低.DEHP低、中、高剂量染毒组与对照组氧化应激反应均具有显著性(p<0.05),且呈剂量效应关系.因此,DEHP可导致小鼠学习记忆能力下降、抑郁情绪增加,其分子机制可能涉及DEHP对脑组织造成的氧化损伤.     

PM2.5 concentration estimation using convolutional neural network and gradient boosting machine

Surface monitoring, vertical atmospheric column observation, and simulation using chemical transportation models are three dominant approaches for perception of fine particles with diameters less than 2.5 micrometers (PM_2.5) concentration. Here we explored an image-based methodology with a deep learning approach and machine learning approach to extend the ability on PM_2.5 perception. Using 6976 images combined with daily weather conditions and hourly time data in Shanghai (2016), trained by hourly surface monitoring concentrations, an end-to-end model consisting of convolutional neural network and gradient boosting machine (GBM) was constructed. The mean absolute error, the root-mean-square error and the R-squared for PM_2.5 concentration estimation using our proposed method is 3.56, 10.02, and 0.85 respectively. The transferability analysis showed that networks trained in Shanghai, fine-tuned with only 10% of images in other locations, achieved performances similar to ones from trained on data from target locations themselves. The sensitivity of different regions in the image to PM_2.5 concentration was also quantified through the analysis of feature importance in GBM. All the required inputs in this study are commonly available, which greatly improved the accessibility of PM_2.5 concentration for placed and period with no surface observation. And this study makes an exploratory attempt on pollution monitoring using graph theory and deep learning approach.     

基于机器学习方法的上海市暴雨内涝灾情预测模型研究

本文基于区域灾害系统理论,综合考虑致灾因子、暴露度和脆弱性,提出了一套暴雨内涝灾情预测指标体系;在此基础上利用上海市应急联动中心110接报暴雨内涝灾情数据,构建了暴雨内涝灾情预测BP模型(Back Propagation Model)和XGBoost模型(Extreme Gradient Boosting Model),并对比分析了预测模型效果,实现对上海市暴雨过程内涝灾情数量预测;最后对内涝灾情影响等级进行阈值划分,以期为暴雨内涝影响预报与风险预警业务、服务及灾害管理提供技术支撑。结果表明:1)综合考虑致灾因子、暴露度、脆弱性指标且不经主成分分析降维的指标组合作为暴雨内涝灾情预测指标体系时,BP模型和XGBoost模型的预测精度最优;2)全量样本XGBoost模型总体表现最优,暴雨内涝灾情的右偏分布和内涝灾情的异常高值均对XGBoost模型预测误差有不同程度的贡献;3)综合评价法在历史灾情百分位法和模拟灾情百分位法基础上,结合多年业务实践经验和用户对于110灾情的处置承受力对暴雨内涝灾情进行阈值划分,在实际应用中具有一定参考意义。     

基于SSA-XGBoost方法的降水变化趋势预测模型

全球变暖预期下不同区域的未来降水变化,是政府和公众都关心的重要问题,也是全球变化研究的前沿科学问题。预测模型的建立是预测/预估研究的重点和难点,现今不稳定的气候背景对预测模型的精准度提出了更高的要求。为了解决传统方法对长期时间序列预测效果欠佳的问题,本文以泰国南部洞穴石笋δ¹⁸O重建的过去270多年（公元1773—2004年）的降水记录为数据集,提出了SSA-XGBoost预测模型。对原始数据去趋势预处理后,采用奇异谱分析法（SSA）提取前部分数据（1773—1964年）的振荡成分以确定数据的最佳谐波个数,并进行准确的周期信号分量分解;之后用去趋势数据减去周期信号得到随机信号,再利用XGBoost模型对随机项进行预测;最后将预测的序列、趋势曲线和周期信号延拓结果相叠加得到最终的预测数据（1965—2004年）。与其他四种模型（XGBoost、ARIMA、SSA-ARIMA、LightGBM）的预测结果相比,SSA-XGBoost的预测结果与真实值最相近,且MAE和RMSE均最小,R²也更接近1,说明该模型具有更高的精度和稳定性。该研究对于泰国南部等热带地区未来的降水变化趋势预测具有较好的指导意义,也可为其他长时间序列的预估研究提供借鉴。     

Using a social learning configuration to increase Vietnamese smallholder farmers’ adaptive capacity to respond to climate change

Social learning is crucial for local smallholder farmers in developing countries to improve their adaptive capacity and to adapt to the current and projected impacts of climate change. While it is widely acknowledged that social learning is a necessary condition for adaptation, few studies have systematically investigated under which conditions particular forms of social learning are most successful in improving adaptive capacity of the most vulnerable groups. This study aims to design, implement and evaluate a social learning configuration in a coastal community in Vietnam. We make use of various methods during four workshop-based interventions with local smallholder farmers: interviews with key farmers and commune leaders, farmer-to-farmer learning, participatory observations and focus group discussions. The methods for evaluation of social learning configuration include in-depth interviews, focus group discussions and structured survey interviews. Our findings show that the social learning configuration used in this study leads to an increased problem ownership, an enhanced knowledge-base with regard to climate change impacts and production adaptation options, improved ability to see connections and interdependencies and finally, strengthened relationships and social cohesion. The results suggest that increased social learning in the community leads to increase in adaptive capacity of smallholder farmers and improves both their economic and environmental sustainability. We discuss the key lessons for designing learning configurations that can successfully enhance adaptive capacity and smallholder farmers’ agency and responsiveness to the challenges posed by climate change impacts.     

Thriving at work: Toward its measurement,construct validation,and theoretical refinement

Thriving is defined as the psychological state in which individuals experience both a sense of vitality and learning. We developed and validated a measure of the construct of thriving at work. Additionally, we theoretically refined the construct by linking it to key outcomes, such as job performance, and by examining its contextual embeddedness. In Study 1, we conducted second‐order confirmatory factor analyses in two samples, demonstrating initial support for the two‐dimensional structure of thriving. We provided evidence for the convergent and discriminant validity of thriving in relation to theoretically related constructs, such as positive and negative affects, learning and performance goal orientations, proactive personality, and core self‐evaluations. In Study 2, across two different samples, we further assessed construct validity by establishing a relationship between thriving and career development initiative, burnout, health, and individual job performance, explaining significant variance beyond traditional attitudinal predictors, such as job satisfaction and organizational commitment. Finally, in Study 3, we focused on understanding the contextual embeddedness of thriving. We found differences in reports of thriving across two points in time, when substantial changes are occurring in peoples' work lives and across contexts (i.e., work and non‐work). Implications for theory and practice, as well as directions for future research, are discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

Learning and the transformative potential of citizen science

The number of collaborative initiatives between scientists and volunteers (i.e., citizen science) is increasing across many research fields. The promise of societal transformation together with scientific breakthroughs contributes to the current popularity of citizen science (CS) in the policy domain. We examined the transformative capacity of citizen science in particular learning through environmental CS as conservation tool. We reviewed the CS and social‐learning literature and examined 14 conservation projects across Europe that involved collaborative CS. We also developed a template that can be used to explore learning arrangements (i.e., learning events and materials) in CS projects and to explain how the desired outcomes can be achieved through CS learning. We found that recent studies aiming to define CS for analytical purposes often fail to improve the conceptual clarity of CS; CS programs may have transformative potential, especially for the development of individual skills, but such transformation is not necessarily occurring at the organizational and institutional levels; empirical evidence on simple learning outcomes, but the assertion of transformative effects of CS learning is often based on assumptions rather than empirical observation; and it is unanimous that learning in CS is considered important, but in practice it often goes unreported or unevaluated. In conclusion, we point to the need for reliable and transparent measurement of transformative effects for democratization of knowledge production.