Strategic planning harnessing urban policy mobilities: the gradual development of local sustainability fix

The aim of our article is to follow how global policy models affect local policy making. Each city has unique local challenges in promoting development, e.g. economic growth, but also needs to find a balance between these targets and demands for sustainable city solutions. In our empirical study, we follow how ideas of waterfront development – to attract new inhabitants and promote economic growth – and global demands of carbon control were used interactively in a strategic spatial planning process in the city of Tampere, Finland. During the six-year planning process, these two policy targets became interdependent, created a new policy-making domain, and led to a combinatorial development of sustainability elements arising from this domain. These findings demonstrate the serial use of global policy models in the creation of a local urban ‘sustainability fix’. To conclude, the intertwinement of diverse global policy models in a city planning process creates easily a recursive cycle that redefines urban sustainability within cities and intercity networks. This perspective makes local policy narratives and strategic planning highly important in urban sustainability research as promoting urban sustainability becomes an inherently ambivalent practice.     

Culture as trigger for sustainability transition in the water domain: the case of the Spanish water policy and the Ebro river basin

There is a mounting body of literature dealing generally with the dynamics of transitions of human systems towards sustainability and specifically with the different stages and processes of transitions. However, the question of why transition processes occur in the first place remains largely unexplained. This paper explores the concept of transition triggers, such as culture or material resource scarcity, and provides a theoretical framework to explain the emergence of a transition and its relation to recent developments in Spanish water policy. We adapt the general framework provided by current transition theory and gather empirical evidence and insights from processes occurring within the Spanish policy context and the Ebro river basin in particular. Our results show that the sole existence of biophysical limits to water use or development cannot explain the start of a possible sustainability transition in this domain in Spain. Changes in the existing water policies in the direction of sustainability were not ignited by people directly affected by water scarcities but by a coalition of sensitive agents, mostly from academia, NGOs and local constituencies, who managed to articulate new identities, integrate multiple sources of policy relevant knowledge, and develop new values under the umbrella of the new water culture movement.

工艺安全绩效指标介绍

介绍了根据瑞士奶酪模型——事故与保护层漏洞所设置的工艺安全绩效指标体系,探讨了美国石油学会标准的工艺安全绩效指标及分级,列举了每一级指标的具体指标和数据收集要求,并具体介绍了杜邦公司的工艺安全绩效指标体系,为国内企业提供参考。     

上海市制药行业废水特征污染因子控制指标分析

制药工业特别是生物制药是上海市六大支柱产业之一,而在制药生产过程中存在着原辅料投入量大产出比小、污染严重的问题。针对制药企业在生产中的原辅材料、工艺过程、产品环节中可能产生有毒有害有机物等进行了调研分析;对比国内外相关排放控制标准,提出了制药工业污废水排放中特征污染因子控制指标的建议。建议补充18种特征污染因子,其中包括1种重金属及无机物、16种有机污染物和1项生物安全因子。     

Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

Introduction: Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity. Method: In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant variables. Results: In terms of balanced accuracy and AUCs, the proposed approach performed reasonably well. Bayesian modeling results indicated that the prior crash/violation experiences of road users and roadways were very important risk indicators. For example, migrant workers tend to have high injury risk due to their dangerous violation behaviors, such as retrograding, red-light running, and right-of-way violation. Furthermore, results showed that certain variable combinations had enhanced impacts on severity outcome than single variables. For example, when a migrant worker and a non-motorized vehicle are involved in a crash happening on a local road with high cumulative violation frequency in the previous year, the probability for drivers suffering serious injury or fatality is much higher than that caused by any single factor. Practical applications: The proposed methodology and modeling results provide insights for developing effective countermeasures to reduce crash severity and improve traffic system safety performance.     

Climate Changea Impacts and Adaptation Strategies in Kenya

To determine the climate changes that are due to natural variability and those due to human activities is quite challenging, just like delineating the impacts. Moreover, it is equally difficult to asce...     

广元市建设川陕甘结合部生态强市的思考

通过对广元市生态市的建设现状、指导思想、奋斗目标、建设路径进行深入分析,提出建设生态强市的可行性措施,并从法制、组织、资金、技术方面提供保障。     

Changes in mineral soil organic carbon stocks in the croplands of European Russia and the Ukraine, 1990–2070; comparison of three models and implications for climate mitigation

Three soil carbon models (RothC, CANDY and the Model of Humus Balance) were used to estimate the impacts of climate change on agricultural mineral soil carbon stocks in European Russia and the Ukraine using detailed spatial data on land-use, future land-use, cropping patterns, agricultural management, climate and soil type. Scenarios of climate were derived from the Hadley Centre climate Version 3 (HadCM3) model; future yields were determined using the Soil–Climate–Yield model, and land use was determined from regional agricultural and economic data and a model of agricultural economics. The models suggest that optimal management, which entails the replacement of row crops with other crops, and the use of extra years of grass in the rotation could reduce Soil organic carbon (SOC) loss in the croplands of European Russia and the Ukraine by 30–44% compared to the business-as-usual management. The environmentally sustainable management scenario (SUS), though applied for a limited area within the total region, suggests that much of this optimisation could be realised without damaging profitability for farmers.     

Framing environmental indicators: moving from causal chains to causal networks

In recent years, there has been a boom in environmental assessment reports utilizing environmental indicators. Most of these publications are based on the casual chain frameworks (e.g., Pressure–State–Response (PSR), Driving force–State–Response (DSR), and Driving force–Pressure–State–Impact–Response (DPSIR)). These frameworks have made an important contribution by emphasising the importance of causality. However, the reliance on simple uni-directional chains is at the same time not very conducive to a good understanding of the complexity of the processes behind environmental indicators. This limits the usefulness of these frameworks for environmental (impact) assessments. In this paper we propose an enhanced DPSIR (eDPSIR) framework for environmental indicators that takes inter-relations of indicators into account by relying on the use of causal networks rather than causal chains. It will be shown how the concept of causal networks can increase insight into the inter-relation of environmental issues and associated indicators, can facilitate the identification of key indicators for particular kinds of questions, and can provide a useful first step to the establishment of dose–response functions. Working with causal networks can contribute to more appropriate environmental policies and better management decisions.