基于倾向值匹配法的城市建成环境对居民生理健康的影响

伴随着城市化和机动化进程,肥胖及其引致的慢性疾病已成为中国严峻的社会问题。本文利用2010年中国社会综合调查、空间兴趣点POI、道路网等数据,探究城市建成环境对居民生理健康状况的影响,并通过倾向值匹配法控制自选择机制的干扰。研究发现：① 高密度的土地利用对居民总体的身体健康状况具有负向影响,而功能混合、支路网通达的城市肌理、以及充足的健康设施在降低身体质量指数BMI、抑制超重和减少慢性病方面发挥着积极作用。② 各类建成环境特征对中高社会阶层的影响集中在主观的健康感知,对中低阶层人群的影响则主要作用于客观的健康指标。③ 小尺度范围内的建成环境与中低阶层群体的身体健康水平存在更为密切的关联,但这一规律在中高社会阶层并不明显。本研究证明了主动式空间干预手段在促进居民生理健康过程中的有效性,进而就建成环境的优化策略提出了初步建议。     

传承和发扬人文与经济地理学前辈的精神和知识体系——人文与经济地理学青年论坛综述

2018年1月27-28日,人文与经济地理学青年论坛暨第二届中国科学院区域可持续发展分析与模拟重点实验室青年论坛在北京国家会议中心成功举办。论坛以“人文与经济地理学的传承与创新：青年学者的责任与行动”为主题,来自全国100余家高校和研究机构从事人文与经济地理学相关研究的青年学者370余人参会。这说明人文与经济地理学青年学者很活跃、有热情、有担当意识。青年学者要加强学习、传承和发扬人文与经济地理学前辈的精神和知识体系,迎接人文与经济地理学繁荣发展的新时代。     

The progress of human geography in China under the support of the National Natural Science Foundation of China

This paper analyzes the role of the National Natural Science Foundation of China (NSFC) in advancing human geography in China by focusing on five key research areas: land use, urban systems and urban agglomeration, economic globalization, climate change and social and cultural geographies. All NSFC-funded human geography programs related to these five topics from 1986 to 2017 comprise the sample for analysis, and the research topics, content, teams, and peer-reviewed journal publications supported by these programs are investigated. Specifically, this paper analyzes the NSFC’s promotion of the expansion of research topics in response to national developmental needs and the shifting frontiers of human geography research internationally, its enhancement of interdisciplinary research, and its contributions to the assembly of specialized research teams. The paper also reports important progress in Chinese human geography over the past 30 years through the institutional lens of the NSFC, revealing major characteristics and trends in the discipline. The paper concludes by calling for further collaboration between the research community and the NSFC for the development of a locally suitable and globally influential Chinese human geography.     

Pesticide-related Food Safety Risks: Farmers’ Self-protective Behavior and Food Safety Social Co-governance

This article investigates the sources of vegetables consumed by farmers, their perception of pesticide-related food safety risks and the behaviors they engage in to protect themselves, and explores the implications for the social co-governance (shehui gongzhi) of food safety emphasized by China’s recent Food Safety Law. The research site is a county in Yunnan Province where vegetable growing is the major source of income and livelihood for local farmers. We surveyed 417 farmers and collected 776 vegetable samples from 377 surveyed farmer households and tested them for organophosphate and carbamate pesticide residues using PR-12N Rapid Detection Instrument for Pesticide Residues. The results showed that farmers know about the risks to food safety caused by pesticides used in vegetable growing and they purposely avoid these risks by mainly consuming vegetables planted in home gardens or private plots that use little or no pesticides. Vegetable samples from these private plots had the lowest positive rate of pesticide residues (6.10%), compared with vegetable samples from commercial farmland (13.73%) and markets (12.66%), and the difference was statistically significant (X²=9.69,0.005相似文献   

Effectiveness of pilot carbon emissions trading systems in China

China is in the process of establishing a national emissions trading system (ETS). Evaluating the implementation effectiveness of the seven pilot ETSs in China is critical for designing this national system. This study administered a questionnaire survey to assess the behaviour of enterprises covered by the seven ETS pilots from the perspective of: the strictness of compliance measures; rules for monitoring, reporting and verification (MRV); the mitigation pressure felt by enterprises; and actual mitigation and trading activities. The results show that the pilot MRV and compliance rules have not yet been fully implemented. The main factors involved are the lack of compulsory force of the regulations and the lack of policy awareness within the affected enterprises. Most enterprises have a shortage of free allowances and thus believe that the ETSs have increased their production costs. Most enterprises have already established mitigation targets. Some of the covered enterprises are aware of their own internal emission reduction costs and most of these have used this as an important reference in trading. Many enterprises have accounted for carbon prices in their long-term investment. The proportion of enterprises that have participated in trading is fairly high; however, reluctance to sell is quite pervasive in the market, and enterprises are mostly motivated to trade simply in order to achieve compliance. Few enterprises are willing to manage their allowances in a market-oriented manner. Different free allowance allocation methods directly affect the pathways enterprises take to control emissions.

Key policy insights

• In the national ETS, the compulsory force of ETS provisions should be strengthened.

• A reasonable level of free allowance shortage should be ensured to promote emission reduction by enterprises.

• Sufficient information should be provided to guide enterprises in their allowance management to activate the market.

• To promote the implementation of mitigation technologies by enterprises, actual output-based allocation methods should be used.

• The government should use market adjustment mechanisms, such as a price floor and ceiling, to ensure that carbon prices are reasonable and stable, so as to guide long-term low carbon investment.

     

An initial scoping of transitional justice for global climate governance

Geopolitical changes combined with the increasing urgency of ambitious climate action have re-opened debates about justice and international climate policy. Tensions about historical responsibility have been particularly difficult and could intensify with increased climate impacts and as developing countries face mounting pressure to take mitigation action. Climate change is not the only time humans have faced historically rooted, collective action challenges involving justice disputes. Practices and tools from transitional justice have been used in over 30 countries across a range of conflicts at the interface of historical responsibility and imperatives for collective futures. Central to this body of theory and experience is the need to reflect both backwards- and forwards-oriented elements in efforts to build social solidarity. Lessons from transitional justice theory and practice have not been systematically explored in the climate context. This article conceptually examines the potential of transitional justice practices to inform global climate governance by looking at the structural similarities and differences between the global climate regime and traditional transitional justice contexts. It then identifies a suite of common transitional justice practices and assesses their potential applicability in the climate context.

POLICY RELEVANCE

• Justice disputes, including about historical responsibility and future climate actions, are long-standing in the climate context and could intensify with increased climate impacts and broadened mitigation pressures.

• Lessons from efforts to use transitional justice mechanisms could provide insight into strategies for balancing recognition of harms rooted in the past, while creating stronger future-oriented collective action.

• Several areas of transitional justice practice including: the combination of amnesties and litigation, truth commissions, reparations and institutional change could provide useful insights for the climate context.

     

Empirical calibration of climate policy using corporate solvency: a case study of the UK’s carbon price support

Emission reductions improve the chances that dangerous anthropogenic climate change will be averted, but could also cause some firms financial distress. Corporate failures, especially if they are unnecessary, add to the social cost of abatement. Social value can be permanently destroyed by the dissolution of organizational capital, deadweight losses paid to liquidators, and unemployment. This article proposes using measures of corporate solvency as an objective tool for policy makers to calibrate the optimal stringency of climate change policies, so that they can deliver the least loss of corporate solvency for a given level of emission reductions. They could also be used to determine the generosity of any compensation to address losses to corporate solvency. We demonstrate this approach using a case study of the UK’s Carbon Price Support (a carbon tax).

Key policy insights

• Solvency metrics could be used to empirically calibrate the optimal stringency of climate policies.

• An idealized solvency trajectory for firms affected by climate change policy would cause corporate solvency to initially decline – approaching but not exceeding ‘distressed’ levels – and then gradually improve to a new ‘steady state’ once the low-carbon transition had been achieved.

• In terms of the UK’s Carbon Price Support, corporate solvency of energy-intensive industries was found to be stable subsequent to its introduction. Therefore, the available evidence does not support its later weakening.

     

Classifying urban land use by integrating remote sensing and social media data

Urban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However, the accurate classification of urban functional zones is challenging due to the complexity of urban systems. Many studies have focused on urban land use classification by considering features that are extracted from either high spatial resolution (HSR) remote sensing images or social media data, but few studies consider both features due to the lack of available models. In our study, we propose a novel scene classification framework to identify dominant urban land use type at the level of traffic analysis zone by integrating probabilistic topic models and support vector machine. A land use word dictionary inside the framework was built by fusing natural–physical features from HSR images and socioeconomic semantic features from multisource social media data. In addition to comparing with manual interpretation data, we designed several experiments to test the land use classification accuracy of our proposed model with different combinations of previously acquired semantic features. The classification results (overall accuracy = 0.865, Kappa = 0.828) demonstrate the effectiveness of our strategy that blends features extracted from multisource geospatial data as semantic features to train the classification model. This method can be applied to help urban planners analyze fine urban structures and monitor urban land use changes, and additional data from multiple sources will be blended into this proposed framework in the future.     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.     

1985-2015年中国省际人口迁移网络特征

随着中国城市经济的发展以及城镇化的快速推进,人口迁移的空间格局发生了重大变化。本文基于1985-2015年全国省际人口迁移矩阵数据,运用社会网络分析方法,探讨中国省际人口迁移网络的整体特征以及每个节点在网络中的地位作用。研究结论为：①省际人口迁移网络密度经历了1985-2000年的缓慢下降、2000-2015年持续上升的过程,与西北、西南和中部主要人口迁出地向东部经济发达地区的集中性迁移成为发展主流密切相关。②人口迁移网络的内向中心势明显大于外向中心势,说明人口迁入地相对集中,人口迁出地相对分散。③省际人口迁移网络可分为东北—华北人口联动区、中原—长三角人口联动区、中南—华南人口联动区、西南人口联动区、西北人口联动区5个子群。其中,中原—长三角人口联动区是群内和群间联系最紧密的区域;中南—华南人口联动区是全国人口累计迁移量最高的区域;东北—华北人口联动区内部联系较强,对外仅与西北人口联动区联系较强。④基于中心性分析以及核心—边缘结构划分,各省(市、区)对于人口迁移网络的贡献意义不同,广东、北京具有全局控制意义,江苏、陕西、四川、浙江基本处于核心节点序列,上海、天津、内蒙古、福建核心作用逐渐增强,河南、湖北、河北、新疆核心作用出现下降,全国人口迁移路径总体向北偏移。