Measurement and assessment of carrying capacity of the environment in Ningbo, China

Carrying Capacity of the Environment (CCE) provides a useful measure of the sustainable development of a region. Approaches that use integrated assessment instead of measurement can lead to misinterpretation of sustainable development because of confusion between Environmental Stress (ES) indexes and CCE indexes, and the selection of over-simple linear plus models. The present paper proposes a comprehensive measurement system for CCE which comprises models of natural resources capacity, environmental assimilative capacity, ecosystem services capacity, and society supporting capacity. The corresponding measurable indexes are designed to assess CCE using a carrying capacity surplus ratio model and a vector of surplus ratio of carrying capacity model. The former aims at direct comparison of ES and CCE based on the values of basic indexes, and the latter uses a Euclidean vector to assess CCE states. The measurement and assessment approaches are applicable to Strategic Environmental Assessment (SEA) and environmental planning and management. A case study is presented for Ningbo, China, whereby all the basic indexes of ECC are measured and the CCE states assessed for 2005 and 2010.     

The effect of environmental regulation on firms' competitive performance: the case of the building & construction sector in some EU regions

There is a considerable debate on the effects of environmental regulation on competitive performance. Based on survey data, this paper analyzesthe two main research questions, derived from literature, on the links between environmental regulation and competitiveness, by focusing on firms operating in the building and construction sector, i.e.: 1) whether environmental policy stringency affects the competitive performance of firms in the building and construction sector 2) and how a specific form of environmental regulation (direct regulation, economic instruments and soft instruments) affects this performance? By applying a regression analysis, we find that a more stringent environmental regulation, measured by inspection frequency, provides a positive impulse for increasing investments in advanced technological equipment and innovative products and on business performance. Moreover, a well-designed “direct regulation” appears to be the most effective policy instrument for prompting the positive impact of environmental policies on innovation and intangible performance while economic instruments do negatively affect business performance.     

Green shipping practices in the shipping industry: Conceptualization, adoption, and implications

There have been increasing concerns about the adverse impacts on the environment caused by cargo movement in international trade. Different stakeholders ranging from shippers and carriers to government bodies and international communities have expressed worries about the environmental impacts brought by shipping related activities. The pollution and waste created in the shipping processes have imposed environmental burdens and accelerated resource depletion. The situation is set to worsen in the face of intensifying trade globalization, which has contributed to sustained growth in international shipping activities. To help protect the environment, many shipping firms have taken the initiative to find ways to lessen the environmental damage of their operations while enhancing their performance. The objective of this study is to examine the environmental awareness and the environmental measures taken in the shipping industry. We propose a conceptual framework for evaluating green shipping practices and develop several propositions stating the conditions under which shipping firms would behave in an environmentally responsible manner. We conclude with managerial and policy implications of the conceptual framework to promote green shipping practices in the shipping industry.     

提升高校环境教育效果的对策

本文论证了高校开展环境教育的必要性,总结了高校环境教育的现状,在分析导致高校环境教育效果差强人意的原因后,为提升高校环境教育效果,提出包括立法在内的一系列对策.     

Balancing durability and environmental impact in concrete combining low-grade recycled aggregates and mineral admixtures

This paper investigated the effect of various amounts of low-grade recycled aggregates in concrete containing mineral admixtures at three different water-binder ratios on mechanical and environmental performance. The balance between durability and environmental impact for a given strength level similar to normal-use concrete was also examined using analytic hierarchy process. Results showed that increasing the water-binder ratio and volume of recycled aggregates reduced compressive strength and increased air permeability and drying shrinkage relative to normal aggregates. However, compared to normal-use concrete similar or better performance could be achieved, which was attributed to improvement of low-grade recycled aggregate performance when combined with fly ash. Similarly, CO₂ emissions and volume of raw materials were lower than the normal-use concrete for all mixes. Concrete mixes with low air permeability and low CO₂ footprint had the best balance of durability and environmental impact, as decreasing raw material volume tended to more greatly reduce durability.     

The causes of food waste in the supplier-retailer interface: Evidences from the UK and Spain

Food and drink waste is a significant problem for economic, environmental and food security reasons. Government efforts have focused on diverting waste away from landfill through regulation, taxation and public awareness. However, efforts to understand why waste occurs have been limited, particularly in the interface between retailers and suppliers. This research aims to address this problem by identifying the main root causes of waste in the supplier-retailer interface.The research is exploratory in nature as there is a paucity of studies focusing on food waste across the supply chain. Data were collected through 43 interviews with managers in food manufacturing, wholesaling and retailing in two countries: the UK (24) and Spain (19). Data from the interviews and supplementary documentation were analyzed using causal maps. This approach helped to identify the main root causes of waste which were categorized into three groups: (1) mega-trends in the marketplace, (2) natural causes related to the products and processes, and (3) management root causes on which practitioners have a direct impact.The paper discusses the root causes of food waste and good practices identified from the interviews. Differences between the UK and Spain are discussed, presenting potential learning opportunities for practitioners in these countries and highlighting opportunities for further research.     

Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain

This paper proposes a mixed performance measurement system using a combination of evolutionary game theory and the balanced scorecard (BSC) in environmental supply chain management (ESCM) that measures and evaluates business operations using the four different perspectives of finance, customer, internal business process, and learning and growth. ESCM plays an important role in the supply chain which leads to the reduction, reuse and recycling of resources involved in both upstream and downstream activities. This paper presents guidance for practical managers in evaluating and measuring ESCM by developing a knowledge-based BSC and evolutionary game theory. The primary purpose of this paper is to apply the proposed method in a case study to one of Iran's biggest auto industry supply chain SAIPA Company. The results of this study indicate that the adoption of ESCM, in the absence of regulatory pressures and cost-saving measures is triggered by public pressures and its implementation is limited by organizational factors and strategic myopia.     

Ecosystem states: Creating a data-derived, ecosystem-scale ecological response model that is explicit in space and time

Increasing difficulties associated with balancing consumptive demands for water and achieving ecological benefits in aquatic ecosystems provide opportunities for new ecosystem-scale ecological response models to assist managers. Using an Australian estuary as a case study, we developed a novel approach to create a data-derived state-and-transition model. The model identifies suites of co-occurring birds, fish, benthic invertebrates and aquatic macrophytes (as ‘states’) and the changing physico-chemical conditions that are associated with each (‘transitions’). The approach first used cluster analysis to identify sets of co-occurring biota. Differences in the physico-chemical data associated with each state were identified using classification trees, with the biotic distinctness of the resultant statistical model tested using analysis of similarities. The predictive capacity of the model was tested using new cases. Two models were created using different time-steps (annual and quarterly) and then combined to capture both longer-term trends and more-recent declines in ecological condition. We identified eight ecosystem states that were differentiated by a mix of water-quantity and water-quality variables. Each ecosystem state represented a distinct biotic assemblage under well-defined physico-chemical conditions. Two ‘basins of attraction’ were identified, with four tidally-influenced states, and another four independent of tidal influence. Within each basin, states described a continuum of relative health, manifest through declining taxonomic diversity and abundances. The main threshold determining relative health was whether freshwater flows had occurred in the region during the previous 339 days. Canonical analyses of principal coordinates tested the predictive capacity of the model and demonstrated that the variance in the environmental data set was well captured (87%) with 52% of the variance in the biological data set also captured. The latter increased to >80% when long- and short-term biological data were analysed separately, indicating that the model described the available data for the Coorong well. This approach thus created a data-derived, multivariate model, where neither states nor transitions were determined a priori. The approach did not over-fit the data, was robust to patchy or missing data, the choice of initial clustering technique and random errors in the biological data set, and was well-received by local natural resource managers. However, the model did not capture causal relationships and requires additional testing, particularly during future episodes of ecological recovery. The approach shows significant promise for simplifying management definitions of ecological condition and, via scenario analyses, can be used to assist in manager decision-making of large, complex aquatic ecosystems in the future.     

Development of a coupled reservoir operation and water diversion model: Balancing human and environmental flow requirements

Maintaining a natural flow regime helps preserve the health of riverine ecosystems. Conventional studies on reservoir operations have focused mainly on identifying optimal operational schemes for satisfying human water demands. To systematically reflect the ecological effects of both natural and human-induced hydrologic alterations, water diversions downstream of the reservoirs should be considered as well. This research focused on a coupled reservoir operation and water diversion (CROWD) model, created through the integration of a reservoir operation model and a water diversion model. The proposed model considers both human and environmental flow requirements, and represents a compromise that balances ecological protection (preservation of the natural flow regime of a river) and human needs (reduced water shortages). In the reservoir operation model, the reservoir space is divided into three zones and different operating rules are developed for directing reservoir operation when water levels are in different zones; in the water diversion model, different water users are assigned different supply priorities with the instream flows no more than the minimum environmental flows having the highest priority; and the two models are coupled by the water mass balance between the two hydraulic facilities. The non-dominated-sorting genetic algorithm II (NSGA-II) was used to determine the parameters of the developed CROWD model and the model was applied to support the joint operational management of the Tanghe Reservoir and the Liaoyang Diversion in the Tang river basin, China. The resulting reservoir operation and water diversion schemes indicate that the CROWD model is useful for optimizing the operation of reservoirs and water diversion schemes. Moreover, it helps to analyze tradeoffs between human and environmental water needs, resulting in solutions that reduce the risk of water shortages and minimize ecological integrity disturbances.     

Incorporating low-resolution historic species location data decreases performance of distribution models

Developing robust species distribution models is important as model outputs are increasingly being incorporated into conservation policy and management decisions. A largely overlooked component of model assessment and refinement is whether to include historic species occurrence data in distribution models to increase the data sample size. Data of different temporal provenance often differ in spatial accuracy and precision. We test the effect of inclusion of historic coarse-resolution occurrence data on distribution model outputs for 187 species of birds in Australian tropical savannas. Models using only recent (after 1990), fine-resolution data had significantly higher model performance scores measured with area under the receiver operating characteristic curve (AUC) than models incorporating both fine- and coarse-resolution data. The drop in AUC score is positively correlated with the total area predicted to be suitable for the species (R² = 0.163-0.187, depending on the environmental predictors in the model), as coarser data generally leads to greater predicted areas. The remaining unexplained variation is likely to be due to the covariate errors resulting from resolution mismatch between species records and environmental predictors. We conclude that decisions regarding data use in species distribution models must be conscious of the variation in predictions that mixed-scale datasets might cause.