Eco‐efficiency Based on Social Performance and its Relationship with Financial Performance

As corporate responsibility for environmental management has gained attention, eco‐efficiency has become recognized as an important concept for improving the social performance of the business sector as well as that of the public sector. Improving eco‐efficiency is widely accepted not only as a means of increasing economic value, but also as a means of reducing environmental effects. However, managing for eco‐efficiency should take into consideration the differences among industries, because the impact of eco‐efficiency on financial and social performance varies among industries. To explore this variation, we conducted a cross‐industry analysis of eco‐efficiency based on social performance using data envelopment analysis (DEA). DEA measures relative efficiency and is a useful tool for taking into account the relative importance of industry‐specific characteristics. Using DEA, eco‐efficiency scores were derived based on the ratio of two factors of social performance: (1) value‐added inducing and production‐inducing economic spillover effects and (2) the amount of greenhouse gases emitted and energy used. Then, we identified the relationships between our eco‐efficiency score and financial performance, which is a measure of the firm's stability. The case study is based on 272 firms in 16 industries in South Korea. Results show that firms in product manufacturing and service‐intensive industries tend to have higher eco‐efficiency scores than those in raw material or chemical‐intensive industries. In addition, most of the industries reveal no relationship between traditional financial performance metrics and eco‐efficiency scores. A handful of industries had significant relationships with one or more financial performance metrics; in some cases, these relationships were negative, whereas in others they were positive. Surprisingly, almost all industries have no significant relationships between eco‐efficiency and financial performance. This result implies that government support for policies that reward firms that attempt to be eco‐efficient are needed, or that other nonfinancial metrics that influence eco‐efficiency, such as employment and brand reputation, should be considered. This article is expected to support policy makers as they formulate industry‐specific environmental strategies.     

Longitudinal Analysis of the Eco‐Design Management Standardization Process in Furniture Companies

This article discusses how eco‐design management standards have been adopted and the environmental and economic results that have been obtained by the Spanish furniture manufacturers. This is precisely the industry sector in Spain where the dissemination of eco‐design standards has been most important. Using multiple case‐study methodology, the research has shown that, in three companies, more than 90% of the environmental impact of the companies’ products occurs within the manufacturing phase. Companies have implemented tools for life cycle assessment with eco‐indicators values that allow them to assess complex products and evaluate their significant environmental impacts at each stage. The environmental strategies of these companies are based on the continuous improvement of the internal processes and the review and monitoring of their activities. In this approach, the proper choice of materials and the environmental management of the supply chain are the main problems for companies. The outcomes achieved by the companies included some improvements, such as a greater control of product management and a reduction in operating costs, that have allowed them to obtain competitive advantages. Moreover, the adoption of standard management has enabled the companies to drive innovation of products, improve the image of companies and their products, significantly reduce the environmental impact of their products, and adapt to new, more demanding environmental laws and regulations.     

Policy Strategies to Promote Eco‐Innovation

This article elaborates an integrated policy framework, including policy features and specific measures that can be implemented to mitigate the barriers to eco‐innovations. We claim that factors inhibiting the development and uptake of eco‐innovations are multifaceted and stem from different sources. This systems view, which highlights the multilayered conception of the obstacles to eco‐innovation, calls for a combination of environmental and technology policies adapted to the different barriers and characteristics of the technologies. Drawing on evolutionary economics, the article sheds light on the appropriate combinations of framework conditions and instruments that are most effective to promote eco‐innovation, considering different barriers and eco‐innovation types (process/product, mature/immature, and radical/incremental).     

Eco‐evolutionary feedbacks between private and public goods: evidence from toxic algal blooms

The importance of ‘eco‐evolutionary feedbacks’ in natural systems is currently unclear. Here, we advance a general hypothesis for a particular class of eco‐evolutionary feedbacks with potentially large, long‐lasting impacts in complex ecosystems. These eco‐evolutionary feedbacks involve traits that mediate important interactions with abiotic and biotic features of the environment and a self‐driven reversal of selection as the ecological impact of the trait varies between private (small scale) and public (large scale). Toxic algal blooms may involve such eco‐evolutionary feedbacks due to the emergence of public goods. We review evidence that toxin production by microalgae may yield ‘privatised’ benefits for individual cells or colonies under pre‐ and early‐bloom conditions; however, the large‐scale, ecosystem‐level effects of toxicity associated with bloom states yield benefits that are necessarily ‘public’. Theory predicts that the replacement of private with public goods may reverse selection for toxicity in the absence of higher level selection. Indeed, blooms often harbor significant genetic and functional diversity: bloom populations may undergo genetic differentiation over a scale of days, and even genetically similar lineages may vary widely in toxic potential. Intriguingly, these observations find parallels in terrestrial communities, suggesting that toxic blooms may serve as useful models for eco‐evolutionary dynamics in nature. Eco‐evolutionary feedbacks involving the emergence of a public good may shed new light on the potential for interactions between ecology and evolution to influence the structure and function of entire ecosystems.     

Eco‐evolutionary feedback promotes Red Queen dynamics and selects for sex in predator populations

Although numerous hypotheses exist to explain the overwhelming presence of sexual reproduction across the tree of life, we still cannot explain its prevalence when considering all inherent costs involved. The Red Queen hypothesis states that sex is maintained because it can create novel genotypes with a selective advantage. This occurs when the interactions between species induce frequent environmental change. Here, we investigate whether coevolution and eco‐evolutionary feedback dynamics in a predator‐prey system allows for indirect selection and maintenance of sexual reproduction in the predator. Combining models and chemostat experiments of a rotifer‐algae system we show a continuous feedback between population and trait change along with recurrent shifts from selection by predation and competition for a limited resource. We found that a high propensity for sex was indirectly selected and was maintained in rotifer populations within environments containing these eco‐evolutionary dynamics; whereas within environments under constant conditions, predators evolved rapidly to lower levels of sex. Thus, our results indicate that the influence of eco‐evolutionary feedback dynamics on the overall evolutionary change has been underestimated.     

Framework for Applying a Complex Adaptive System Approach to Model the Operation of Eco‐Industrial Parks

Models of eco‐industrial parks (EIPs) might help us transform our production systems by fostering the emergence of sustainable EIPs since such models have the potential to support the decision‐making processes of cooperative companies that participate and to decrease operational uncertainties. In this article, a conceptual framework for modeling the operation of EIPs is presented. The framework is underpinned by complex adaptive systems theory, industrial ecology, and an analysis of the experiences of existing EIPs. The proposed framework draws on the observed strengths of two types of industrial symbiosis models—planned eco‐industrial parks (PEIPs) and EIPs that developed through self‐organizing symbiosis (SOS)—as well as their observed weaknesses and the features of complex adaptive systems. From this analysis, five key properties to be modeled are deduced: functionality, reliability, life span, theoretical knowledge, and adaptability. It is proposed that the properties of functionality and theoretical knowledge are determined by the goals of the EIP and its member companies, while the property of adaptability is determined by the understanding that the companies in an EIP have of the environment surrounding the EIP, while the properties of reliability and life span are determined by the internal and external relationships of the companies that make up an EIP.     

The genomic basis of eco‐evolutionary dynamics

Recent recognition that ecological and evolutionary processes can operate on similar timescales has led to a rapid increase in theoretical and empirical studies on eco‐evolutionary dynamics. Progress in the fields of evolutionary biology, genomics and ecology is greatly enhancing our understanding of rapid adaptive processes, the predictability of adaptation and the genetics of ecologically important traits. However, progress in these fields has proceeded largely independently of one another. In an attempt to better integrate these fields, the centre for ‘Adaptation to a Changing Environment’ organized a conference entitled ‘The genomic basis of eco‐evolutionary change’ and brought together experts in ecological genomics and eco‐evolutionary dynamics. In this review, we use the work of the invited speakers to summarize eco‐evolutionary dynamics and discuss how they are relevant for understanding and predicting responses to contemporary environmental change. Then, we show how recent advances in genomics are contributing to our understanding of eco‐evolutionary dynamics. Finally, we highlight the gaps in our understanding of eco‐evolutionary dynamics and recommend future avenues of research in eco‐evolutionary dynamics.     

Mixed‐Effects State‐Space Models for Analysis of Longitudinal Dynamic Systems

Summary The rapid development of new biotechnologies allows us to deeply understand biomedical dynamic systems in more detail and at a cellular level. Many of the subject‐specific biomedical systems can be described by a set of differential or difference equations that are similar to engineering dynamic systems. In this article, motivated by HIV dynamic studies, we propose a class of mixed‐effects state‐space models based on the longitudinal feature of dynamic systems. State‐space models with mixed‐effects components are very flexible in modeling the serial correlation of within‐subject observations and between‐subject variations. The Bayesian approach and the maximum likelihood method for standard mixed‐effects models and state‐space models are modified and investigated for estimating unknown parameters in the proposed models. In the Bayesian approach, full conditional distributions are derived and the Gibbs sampler is constructed to explore the posterior distributions. For the maximum likelihood method, we develop a Monte Carlo EM algorithm with a Gibbs sampler step to approximate the conditional expectations in the E‐step. Simulation studies are conducted to compare the two proposed methods. We apply the mixed‐effects state‐space model to a data set from an AIDS clinical trial to illustrate the proposed methodologies. The proposed models and methods may also have potential applications in other biomedical system analyses such as tumor dynamics in cancer research and genetic regulatory network modeling.     

Homogeneity/Heterogeneity Hypotheses for Standardized Mortality Ratios Based on Minimum Power‐divergence Estimators

This paper analyzes the power divergence estimators when homogeneity/heterogeneity hypotheses among standardized mortality ratios (SMRs) are taken into account. A Monte Carlo study shows that when the standard mortality rate is not external, that is it is estimated from the sample data, these estimators have a good performance even for small sample sets and in particular the minimum chi‐square estimators have a better behavior compared to the classical maximum likelihood estimators. In order to make decisions under homogeneity/heterogeneity hypotheses of SMRs we propose some test‐statistics which consider the minimum power divergence estimators. Through a numerical example focused on SMRs of melanoma mortality ratios in different regions of the US, a homogeneity/heterogeneity study is illustrated.     

Industrial Waste Reuse and By‐product Synergy Optimization

By‐product synergy is a growing practice worldwide. It consists in the maximization of resources utilization with the replacement of raw materials by by‐products as inputs for industrial processes. In order to support decision making in such strategic projects, appropriate tools must be developed. This article presents the results of a research project, which includes the development of a multiobjective mathematical programming model for the optimization of by‐product flows, synergy configurations, and investment decisions in eco‐industrial networks. This model is evaluated using data related to the Kalundborg industrial symbiosis (IS) in order to illustrate its utilization, as well as to assess, in a retrospective manner, the behavior of the companies involved with respect to both economic and environmental benefits of synergies. The experiments also illustrate the influence of the municipality on synergy implementation and how a scenario‐based approach can be used to anticipate raw material price increase. The results are generally coherent with the actual timing of synergy initializations. Further, the considerable effect of water price on the length of investments’ payback period illustrates the impact of policies and regulations on IS.     

A group sequential type design for three‐arm non‐inferiority trials with binary endpoints

The three‐arm design with a test treatment, an active control and a placebo group is the gold standard design for non‐inferiority trials if it is ethically justifiable to expose patients to placebo. In this paper, we first use the closed testing principle to establish the hierarchical testing procedure for the multiple comparisons involved in the three‐arm design. For the effect preservation test we derive the explicit formula for the optimal allocation ratios. We propose a group sequential type design, which naturally accommodates the hierarchical testing procedure. Under this proposed design, Monte Carlo simulations are conducted to evaluate the performance of the sequential effect preservation test when the variance of the test statistic is estimated based on the restricted maximum likelihood estimators of the response rates under the null hypothesis. When there are uncertainties for the placebo response rate, the proposed design demonstrates better operating characteristics than the fixed sample design.     

Empirical‐likelihood‐based criteria for model selection on marginal analysis of longitudinal data with dropout missingness

Longitudinal data are common in clinical trials and observational studies, where missing outcomes due to dropouts are always encountered. Under such context with the assumption of missing at random, the weighted generalized estimating equation (WGEE) approach is widely adopted for marginal analysis. Model selection on marginal mean regression is a crucial aspect of data analysis, and identifying an appropriate correlation structure for model fitting may also be of interest and importance. However, the existing information criteria for model selection in WGEE have limitations, such as separate criteria for the selection of marginal mean and correlation structures, unsatisfactory selection performance in small‐sample setups, and so forth. In particular, there are few studies to develop joint information criteria for selection of both marginal mean and correlation structures. In this work, by embedding empirical likelihood into the WGEE framework, we propose two innovative information criteria named a joint empirical Akaike information criterion and a joint empirical Bayesian information criterion, which can simultaneously select the variables for marginal mean regression and also correlation structure. Through extensive simulation studies, these empirical‐likelihood‐based criteria exhibit robustness, flexibility, and outperformance compared to the other criteria including the weighted quasi‐likelihood under the independence model criterion, the missing longitudinal information criterion, and the joint longitudinal information criterion. In addition, we provide a theoretical justification of our proposed criteria, and present two real data examples in practice for further illustration.     

Augmented Cross‐Sectional Prevalence Testing for Estimating HIV Incidence

Summary Estimation of an HIV incidence rate based on a cross‐sectional sample of individuals evaluated with both a sensitive and less‐sensitive diagnostic test offers important advantages to incidence estimation based on a longitudinal cohort study. However, the reliability of the cross‐sectional approach has been called into question because of two major concerns. One is the difficulty in obtaining a reliable external approximation for the mean “window period” between detectability of HIV infection with the sensitive and less‐sensitive test, which is used in the cross‐sectional estimation procedure. The other is how to handle false negative results with the less‐sensitive diagnostic test; that is, subjects who may test negative—implying a recent infection—long after they are infected. We propose and investigate an augmented design for cross‐sectional incidence estimation studies in which subjects found in the recent infection state are followed for transition to the nonrecent infection state. Inference is based on likelihood methods that account for the length‐biased nature of the window periods of subjects found in the recent infection state, and relate the distribution of their forward recurrence times to the population distribution of the window period. The approach performs well in simulation studies and eliminates the need for external approximations of the mean window period and, where applicable, the false negative rate.     

Measuring and Improving Eco‐efficiency Using Data Envelopment Analysis

The concept of eco‐efficiency can be defined with the “product value/environmental influence” ratio. Different models have been proposed to measure eco‐efficiency. The main difference among them is the weighting system used to aggregate the environmental results. Data envelopment analysis (DEA) permits this aggregation without requiring a subjective judgment about the weights. In this study, we applied a DEA model to Spanish Mahón‐Menorca cheese production to determine the most eco‐efficient production techniques. To this end, 16 scenarios of Mahón‐Menorca cheese production were built regarding technical (degree of automation) and cleaner production criteria. The environmental impacts were assessed by means of life cycle assessment. We carried out an economic assessment by determining the economic value added and the net income for each scenario. The results are referred to as 1 kilogram (kg) cheese ripened over 105 days. Through DEA, an eco‐efficiency ratio between 0 and 1 was obtained. Three scenarios were found to be eco‐efficient, with a high degree of automation (enclosed vat and molding and demolding machines) and accelerated cheese ripening. We used Monte Carlo simulation to carry out a sensitivity analysis to compare the influence of price changes on the eco‐efficiency ratio. The results emphasize the consistency and stability of the eco‐efficient scenarios.     

En route to economical eco‐friendly solvent system in enhancing sustainable recovery of poly(3‐hydroxybutyrate‐co‐4‐hydroxybutyrate) copolymer

Separation of poly(3‐hydroxybutyrate‐co‐4‐hydroxybutyrate) [P(3HB‐co‐4HB)] from bacterial cell matter is a critical step in the downstream process with respect to material quality and eco‐balance as P(3HB‐co‐4HB) is widely used for biomedical applications. Therefore, an efficient and eco‐based extraction of P(3HB‐co‐4HB) using a combination of NaOH and Lysol in digesting the non‐polymeric cell material (NPCM) digestion is developed. The NaOH and Lysol show synergistic influence on the copolymer extraction at a high purity and recovery of 97 and 98 wt% respectively. The optimized cell digestion method was found applicable to a vast batch of cells containing copolymers from various 4HB monomer compositions. At the largest extraction volume of 100 L, P(3HB‐co‐4HB) with a purity of 89 wt% was extracted with a maximum recovery of 90 wt%. The method developed has also eliminated the cell pretreatment step. The extraction method developed in this research has not only produced an economic and efficient copolymer recovery but has also retained the copolymer quality, in term of its molecular weight and thermal properties. It demonstrates a practical and promising downstream processing method in recovering the copolymer effectively from the bacterial biomass.     

Willingness to Pay for Eco‐Certified Refurbished Products: The Effects of Environmental Attitudes and Knowledge

Refurbishing products, which are increasingly sold in business‐to‐consumer markets, is a key strategy to reduce waste. Nevertheless, research finds that consumers’ willingness to pay (WTP) for refurbished products is low. Strategies for a higher WTP are needed in order to grow consumer markets for refurbished products. Eco‐certification of refurbished products may be a key strategy here. Drawing on the consumer WTP literature concerning “green” products, we investigate the impact of independent eco‐certificates. Our analysis is based on a survey of 231 potential customers. The results suggest that, across various product categories, the WTP for products with refurbished components is significantly lower. Adding an eco‐certificate tends to return the WTP toward the virgin product level. We show that consumers with proenvironmental attitudes particularly exhibit green buying behavior. Our findings indicate that eco‐certification is often worthwhile because it enhances the business rationale for producing products with refurbished components.     

Towards an eco‐phylogenetic framework for infectious disease ecology

Identifying patterns and drivers of infectious disease dynamics across multiple scales is a fundamental challenge for modern science. There is growing awareness that it is necessary to incorporate multi‐host and/or multi‐parasite interactions to understand and predict current and future disease threats better, and new tools are needed to help address this task. Eco‐phylogenetics (phylogenetic community ecology) provides one avenue for exploring multi‐host multi‐parasite systems, yet the incorporation of eco‐phylogenetic concepts and methods into studies of host pathogen dynamics has lagged behind. Eco‐phylogenetics is a transformative approach that uses evolutionary history to infer present‐day dynamics. Here, we present an eco‐phylogenetic framework to reveal insights into parasite communities and infectious disease dynamics across spatial and temporal scales. We illustrate how eco‐phylogenetic methods can help untangle the mechanisms of host–parasite dynamics from individual (e.g. co‐infection) to landscape scales (e.g. parasite/host community structure). An improved ecological understanding of multi‐host and multi‐pathogen dynamics across scales will increase our ability to predict disease threats.     

The virtues and limitations of exploring the eco‐evolutionary dynamics of sexually selected traits

Most studies on eco‐evolutionary feedbacks concern the influence of abiotic factors, or predator–prey and host–parasite interactions, while studies involving sexual interactions are lagging behind. This is at odds with the potential of these interactions to engage in such processes. Indeed, there is now ample evidence that sexual selection is affected by ecological change and that sexually selected traits can evolve rapidly, which may modify the ecological context of populations, and thus the selection pressures they will be exposed to. Here we review evidence for such eco‐evolutionary processes. We discuss examples of eco‐evolutionary change in an attempt to understand the challenges related with identifying and characterizing such processes. In particular, we focus on the challenges associated with accurately identifying the components of the feedback as well as their causal relation. Finally, we evaluate scenarios where understanding eco‐evolutionary feedbacks of sexual selection may help us appreciate the effects of sexual selection in shaping evolutionary processes.     

Modeling and Optimization of a Coal‐Chemical Eco‐industrial System in China

China is coal dependent, and this situation will persist for a long time. Because more and more attention has been paid to energy security, the coal‐chemical industry has become a hot topic and has developed quickly. To improve efficiency and reduce emissions, industrial symbiosis (IS) can be introduced when establishing a coal‐chemical eco‐industrial system to achieve harmonious development between nature, industry, and society. In order to learn the influence of IS on the current industrial system, a model of coal‐chemical eco‐industrial systems was built. Using scenario optimization and linear programming, the behaviors and optimal industrial structures of the system under different scenario settings were compared, and industrial ecological analysis was performed. By comparative analysis, results showed that the greatest proportional decrease in the use of coal for coking was 15% compared with actual data for 2005. The resource‐productivity and eco‐productivity were 828 yuan/ton and 2.51, which are much higher than the values of 548 yuan/ton and 1.23 in 2005. The symbiosis index and the link density were found to be 0.675 and 1.67, compared with 0.588 and 0.94 in 2005. Research results showed that the coal‐chemical eco‐industrial system achieved a high value‐added utilization of coal and an updated product profile. Such systems will constitute the main direction and the inevitable trend of China's coal utilization in the future, which will reduce the harm to the environment from increased coal use and benefit the energy industry, the economy, and society.     

When and where plant‐soil feedback may promote plant coexistence: a meta‐analysis

Plant‐soil feedback (PSF) theory provides a powerful framework for understanding plant dynamics by integrating growth assays into predictions of whether soil communities stabilise plant–plant interactions. However, we lack a comprehensive view of the likelihood of feedback‐driven coexistence, partly because of a failure to analyse pairwise PSF, the metric directly linked to plant species coexistence. Here, we determine the relative importance of plant evolutionary history, traits, and environmental factors for coexistence through PSF using a meta‐analysis of 1038 pairwise PSF measures. Consistent with eco‐evolutionary predictions, feedback is more likely to mediate coexistence for pairs of plant species (1) associating with similar guilds of mycorrhizal fungi, (2) of increasing phylogenetic distance, and (3) interacting with native microbes. We also found evidence for a primary role of pathogens in feedback‐mediated coexistence. By combining results over several independent studies, our results confirm that PSF may play a key role in plant species coexistence, species invasion, and the phylogenetic diversification of plant communities.