Carbon market and technology transfer: statistical analysis for exploring implications

The impact of clean development mechanism (CDM) projects on climate change technology transfer (CCTT), which is essential for developing countries to achieve higher mitigation targets and move towards more sustainable paths, has been, until now, inadequately understood and analysed. The aim of this paper is to analyse the carbon market contribution to CCTT, mainly through the CDM, so as to stimulate developing countries towards the deployment and diffusion of low-carbon technologies that fulfil their sustainability goals. Indeed, relatively few studies deal with the assessment of the CDM contribution to CCTT, mainly through desk analysis and empirical evaluations on project design documents. To the best of our knowledge, there are no studies dealing with CCTT through CDM projects using statistical approaches. The added value of this article is the use of statistical analysis, time series analysis and multiple linear regression to analyse carbon market experiences in selected cross-representative developing countries. This assessment indicated the very heterogeneous CCTT across CDM project types, varying significantly in terms of reliance on imported technology, mix of equipment and knowledge and source countries for the technology.     

Constructing meaningful environmental indices: A nonparametric frontier approach

Environmental information disclosure programs seek to motivate firms to reduce their environmental impact. A variety of environmental impacts are reported in these programs and often this information is aggregated into a composite environmental index (CEI) for easier communication. The challenge is to create a meaningful index that allows environmental performance to be compared over time and space without ambiguity. In this paper, we argue that it is important to develop a cardinally meaningful and standardized CEI and use a nonparametric frontier approach to constructing such an index. This approach has the advantage to handle issues associated with data irregularity and the mixed measurability of underlying variables. We apply this approach to constructing a CEI for evaluating the environmental performance of manufacturing facilities in different industrial sectors in Los Angeles based on data from the toxic release inventory. We show how the CEI can be used to improve facility-level environmental performance. A sensitivity analysis is conducted with respect to the uncertainty in data accuracy, which demonstrates the robustness of the nonparametric frontier approach in constructing meaningful environmental indices.     

Quantifying biological integrity by taxonomic completeness: its utility in regional and global assessments.

Water resources managers and conservation biologists need reliable, quantitative, and directly comparable methods for assessing the biological integrity of the world's aquatic ecosystems. Large-scale assessments are constrained by the lack of consistency in the indicators used to assess biological integrity and our current inability to translate between indicators. In theory, assessments based on estimates of taxonomic completeness, i.e., the proportion of expected taxa that were observed (observed/expected, O/E) are directly comparable to one another and should therefore allow regionally and globally consistent summaries of the biological integrity of freshwater ecosystems. However, we know little about the true comparability of O/E assessments derived from different data sets or how well O/E assessments perform relative to other indicators in use. I compared the performance (precision, bias, and sensitivity to stressors) of O/E assessments based on five different data sets with the performance of the indicators previously applied to these data (three multimetric indices, a biotic index, and a hybrid method used by the state of Maine). Analyses were based on data collected from U.S. stream ecosystems in North Carolina, the Mid-Atlantic Highlands, Maine, and Ohio. O/E assessments resulted in very similar estimates of mean regional conditions compared with most other indicators once these indicators' values were standardized relative to reference-site means. However, other indicators tended to be biased estimators of O/E, a consequence of differences in their response to natural environmental gradients and sensitivity to stressors. These results imply that, in some cases, it may be possible to compare assessments derived from different indicators by standardizing their values (a statistical approach to data harmonization). In situations where it is difficult to standardize or otherwise harmonize two or more indicators, O/E values can easily be derived from existing raw sample data. With some caveats, O/E should provide more directly comparable assessments of biological integrity across regions than is possible by harmonizing values of a mix of indicators.     

Ecosystem network analysis indicators are generally robust to parameter uncertainty in a phosphorus model of Lake Sidney Lanier, USA

Understanding how data uncertainty influences ecosystem analysis is critical as we move toward ecosystem-based management. Here, we investigate how 18 Ecological Network Analysis (ENA) indicators that characterize ecosystem growth, development, and condition are affected by uncertainty in an ecosystem model of Lake Sidney Lanier (USA). We applied ENA to 122 plausible parameterizations of the ecosystem developed by Borrett and Osidele (2007, Ecological Modelling 200, 371-387), and then used the coefficient of variation (CV) to compare system indicator variability. We considered Total System Throughput (TST) as a measure of the underlying model uncertainty and tested three hypotheses. First, we hypothesized that non-ratio indicators whose calculation includes the TST would be at least as variable as TST if not more variable. Second, we postulated that indicators calculated as ratios, with TST in the numerator and denominator would tend to be less variable than TST because its influence will cancel. Last, we expected the Average Mutual Information (AMI) to be less variable than TST because it is a bounded function. Our work shows that the 18 indicators grouped into four categories. The first group has significantly larger CVs than the CV for TST. In this group, model uncertainty is amplified rendering these three indicators less useful. The second group of four indicators shows no significant difference in variability with respect to TST. Finally, there are two groups whose CV values are significantly lower than that for TST. The least variable group includes the ratio-based indicators and Average Mutual Information. Due to their low variability, we conclude that these indicators are the most robust to the parameter uncertainty and most useful for ecosystem assessment and comparative ecosystem analysis. In summary, this work suggests that we can be as certain, or more certain, in most of the selected ENA indicators as we are in the parameters of the model analyzed.     

Sustainability indicators for environmental performance and sustainability assessment of the productions of four fine Italian wines

Increased awareness of the importance of environmental protection and the introduction of international standards like ISO 14001 stimulated development of environmental sustainability indicators as a means to measure systems environmental performance. Here, a thermodynamic approach, emergy analysis (Odum, 1988) was used to obtain sustainability indicators able to analyse and quantify the productive and ecological performance of four famous Italian wine productions: Chianti, Brunello di Montalcino, Nobile di Montepulciano, and Barbera d'hti. The application to agricultural production was motivated by the growing need for productive and environmentally sound development in modern agriculture, in which the resource base, the environment, is preserved. The choice of wines, more precisely, grape production, is due to their importance for Italian agriculture and the economy. All the wines demonstrated a good long-term environmental sustainability, especially in view of their high quality and in comparison with the average Italian wine production. Emergy analysis proved a powerful tool for assessing environmental performance of these systems, and its use could easily be extended to other wine productions to obtain a form of environmental performance classification of this product. Systematic use of emergy analysis to assess environmental performance of different processes and products, not necessarily in agriculture, could be useful for environmental certification.     

Ecological Uses of Vertebrate Indicator Species: A Critique

Abstract: Plant and animal species have been used for decades as indicators of air and water quality and agricultural and range conditions. Increasingly, vertebrates are used to assess population trends and habitat quality for other species. In this paper we review the conceptual bases, assumptions, and published guidelines for selection and use of vertebrates as ecological indicators. We conclude that an absence of precise definitions and procedures, confounded criteria used to select species, and discordance with ecological literature severely weaken the effectiveness and credibility of using vertebrates as ecological indicators. In many cases the use of ecological indicator species is inappropriate, but when necessary, the following recommendations will make their use more rigorous: (1) clearly state assessment goals, (2) use indicators only when other assessment options are unavailable, (3) choose indicator species by explicitly defined criteria that are in accord with assessment goals, (4) include all species that fulfill stated selection criteria (5) know the biology of the indicator in detail, and treat the indicator as a formal estimator in conceptual and statistical models, (6) identify and define sources of subjectivity when selecting monitoring and intetpreting indicator species, (7) submit assessment design, methods of data collection and statistical analysis, interpretations, and recommendations to peer review and (8) direct research at developing an overall strategy for monitoring wildlife that accounts for natural variability in population attributes and incorporates concepts from landscape ecology.     

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.     

Social development benefits of hydroelectricity CDM projects in Brazil

In recent years, the concept of sustainable development (SD) has become increasingly recognized and important. Within organizations, SD is often portrayed as a balancing act and requires a combination of three elements to be considered: economy, environment, and society. Traditionally, organizational management research has been focused on economical and environmental fronts. However, social aspects are also important for organizations, especially those in emerging and developing countries. The goal of this article is to investigate the potential of Clean Development Mechanism (CDM) projects to deliver social benefits in Brazil’s hydroelectricity sector. The investigation involved the assessment of 46 registered hydro CDM projects under the Kyoto Protocol in terms of their potential impact on the envisaged social development goals. Two case studies were also examined. Results indicate that organizations managing hydroelectric initiatives in Brazil can provide the pathway toward achieving a number of important social benefits. Successful projects were found to have good community involvement and were managed by both cooperative ventures and money-making corporations. The research also identified several challenges that are hindering hydro CDM projects from delivering more social benefits and enabled a number of recommendations to be extracted for the organizations facing these challenges.     

Text analysis tools for identification of emerging topics and research gaps in conservation science

Keeping track of conceptual and methodological developments is a critical skill for research scientists, but this task is increasingly difficult due to the high rate of academic publication. As a crisis discipline, conservation science is particularly in need of tools that facilitate rapid yet insightful synthesis. We show how a common text‐mining method (latent Dirichlet allocation, or topic modeling) and statistical tests familiar to ecologists (cluster analysis, regression, and network analysis) can be used to investigate trends and identify potential research gaps in the scientific literature. We tested these methods on the literature on ecological surrogates and indicators. Analysis of topic popularity within this corpus showed a strong emphasis on monitoring and management of fragmented ecosystems, while analysis of research gaps suggested a greater role for genetic surrogates and indicators. Our results show that automated text analysis methods need to be used with care, but can provide information that is complementary to that given by systematic reviews and meta‐analyses, increasing scientists’ capacity for research synthesis.     

Multicriteria prioritization and partial order in environmental sciences

The complexity of the present data-centric world finds its expression in the increasing number of multi-indicator systems. This has led to the development of multicriteria ranking systems based on partial orders. Order theory is a main pillar of structural mathematics. Partial orders help to reveal why an object of interest holds a certain ranking position and how much it is subject to change if a composite indicator is upgraded. Order theory helps to derive linear or weak orders without indicator weighting schemes. Hence, rankings obtained from decision support systems (DSS) which depend on many parameters beyond the data matrix can be checked and discrepancies can lead to examine the parameters of the DSS. Order theory helps discover association and implication structures derived from formal concept lattices. Association and implication networks among the attributes of the data matrix allow more insights into multi-indicator systems and lead to new hypotheses and motivate further research. Some new and innovative concepts, like separated subsets, antagonistic indicators, ranking stability fields are rendered. Separated subsets are the typical outcome of a partial order analysis; their identification leads to antagonistic indicators, which are responsible for the separatedness of object’s subsets. Numerical aggregation can be performed step-by-step and the question which values of a weight lead to an order inversion is of high interest. The concept of stability fields is one possible answer, discussed in this paper. After an outline of partial order theory some more specific theoretical results are shown, then we discuss the role of composite indicators in the light of partial order and give some examples of interesting applications of partial order. Finally examples are selected from real life case studies of watersheds, environmental performance evaluations, child well being, geographic and administrative regions and more.     

The measurement of green finance index and the development forecast of green finance in China

This paper proposes a green finance index that may help policymakers and investors take more favorable actions based on the development of green finance. After analysis and organization of the development process of green finance and related green finance and index concepts, this paper uses the improved fuzzy comprehensive evaluation method to construct a measurement model suitable for measuring the development level of green finance based on the principle of fuzzy mathematics. The index weight adopts the entropy method and improved Analytic Hierarchy Process (AHP) joint determination. At the same time, using the relevant statistical indicators of China's green credit from 2011 to 2019, and using the constructed model, the level of China's green finance development during this period was evaluated. Finally, the obtained data and classical gray model methods were used to predict China's green development level from 2020 to 2024. The research results show that: This model is a good measure of the level of development of green finance, and China's green finance index has generally shown a rapid growth trend over the past nine years, with the fastest growth rate between 2013 and 2014. From the perspective of the weight of each index affecting the green financial index, the weight of new energy, green transportation projects and new energy vehicles ranked in the top three, and the impact of these three indexes on China's green financial index is significant. In the future, China's green financial development level will continue to improve.

     

The effectiveness of cluster approach to improve environmental corporate performance in an industrial district of SMEs: a case study

This paper aims to assess the effectiveness of the so-called ‘Eco-Management and Audit Scheme (EMAS) cluster approach’ when applied to environmental policies, by focusing on the case history of the industrial paper production cluster located in the Province of Lucca (Italy). The Lucca cluster represents approximately 20% of Italian paper production, and Italy is the fourth leading paper producer in Europe. In the last 10 years, environmental policies have been developed under the common ‘umbrella’ of a strong public and private partnership based on stakeholder networking within the application of the EU EMAS Regulation. This article evaluates the outcome of such an approach, by comparing the environmental performance indicators for the Lucca’s paper industry using data collected from more than 40 plants before and after the adoption of this voluntary tool. The results show considerable improvements for many environmental performance indicators, consistently with the implementation of the cluster approach.     

Environ indicator sensitivity to flux uncertainty in a phosphorus model of Lake Sidney Lanier, USA

Effective environmental impact assessment and management requires improved understanding of the organization and transformation of ecosystems in which independent agents are linked through an intricate network of energy, matter, and informational interactions. While advances have been made, we still lack a complete understanding of the processes that create, constrain, and sustain ecosystems. Network environ analysis (NEA) provides one approach for building novel ecosystem insights, but it is model dependent. As ecological modeling is an imprecise art, often complicated by inadequate empirical data, the utility of NEA may be limited by model uncertainty. Here, we investigate the sensitivity of NEA indicators of ecosystem growth and development to flow and storage uncertainty in a phosphorus model of Lake Sidney Lanier, USA. The indicators are total system throughflow (TST), total system storage (TSS), total boundary input (Boundary), Finn cycling index (FCI), ratio of indirect-to-direct flows (Indirect/Direct), indirect flow index (IFI), network aggradation (AGG), network homogenization (HMG), and network amplification (AMP). Our results make two primary contributions. First, they demonstrate that five of the indicators – FCI, Indirect/Direct, IFI, AGG and HMG – are relatively robust to the flow and storage uncertainty in the Lake Lanier model. This stability lets us draw robust conclusions about the Lake Lanier ecosystem organization (e.g., phosphorus flux in the lake is dominated by internal processes) in spite of uncertainties in the model. Second, we show that the majority of the indicators co-vary and that most of their common variation could be mapped onto two latent factors, which we interpret as (1) system integration and (2) boundary influences.     

Fecundity as a Basis for Risk Assessment of Nonindigenous Freshwater Molluscs

Abstract:  The most efficient way to reduce future damages from nonindigenous species is to prevent the introduction of harmful species. Although ecologists have long sought to predict the identity of such species, recent methodological advances promise success where previous attempts failed. We applied recently developed risk assessment approaches to nonindigenous freshwater molluscs at two geographic scales: the Laurentian Great Lakes basin and the 48 contiguous states of the United States. We used data on natural history and biogeography to discriminate between established freshwater molluscs that are benign and those that constitute nuisances (i.e., cause environmental and/or economic damage). Two statistical techniques, logistic regression and categorical tree analysis, showed that nuisance status was positively associated with fecundity. Other aspects of natural history and biogeography did not significantly affect likelihood of becoming a nuisance. We then used the derived statistical models to predict the chance that 15 mollusc species not yet in natural ecosystems would cause damage if they become established. We also tested whether time since establishment is related to the likelihood that nonindigenous mollusc species in the Great Lakes and United States would cause negative impacts. No significant relationship was evident at the U.S. scale, but recently established molluscs within the Great Lakes were more likely to cause negative impacts. This may reflect changing environmental conditions, changing patterns of trade, or may be an indication of "invasional meltdown." Our quantitative analyses could be extended to other taxa and ecosystems and offer a number of improvements over the qualitative risk assessments currently used by U.S. (and other) government agencies.     

Environmental indicators GIS of the Catalan coast

This paper describes the environmental indicators GIS of the Catalan coast, Spain. The spatial data model is based on vector and raster layers with three main modules: the biodiversity, the general biophysical and the socioeconomic. Presently, the database has a number of pressure and impact indicators that have been used to model the components and structure of the system and are suggested to build ecological resilience. Special interest has been given to the analysis of functional groups of species that are relevant to the dynamics of the coastal system, and preliminary results are presented. This system constitutes a user-oriented analytical and monitoring tool for coastal zone managers and researchers. Although, the system is under development it is expected that resulting spatial indicators of environmental condition can be used to promote more sustainable coastal strategies and actions in the Catalan coast.     

Statistics: an essential technology in environmental research and management

Statistical methods as developed and used in decision making and scientific research are of recent origin. The logical foundations of statistics are still under discussion and some care is needed in applying the existing methodology and interpreting results. Some pitfalls in statistical data analysis are discussed and the importance of cross examination of data (or exploratory data analysis) before using specific statistical techniques are emphasized. Comments are made on the treatment of outliers, choice of stochastic models, use of multivariate techniques and the choice of software (expert systems) in statistical analysis. The need for developing new methodology with particular relevance to environmental research and policy is stressed.Dr Rao is Eberly Professor of Statistics and Director of the Penn State Center for Multivariate Analysis. He has received PhD and ScD degrees from Cambridge University, and has been awarded numerous honorary doctorates from universities around the world. He is a Fellow of Royal Society, UK; Fellow of Indian National Science Academy; Foreign Honorary Member of American Academy of Arts and Science; Life Fellow of King's College, Cambridge; and Founder Fellow of the Third World Academy of Sciences. He is Honorary Fellow and President of International Statistical Institute, Biometric Society and elected Fellow of the Institute of Mathematical Statistics. He has made outstanding contributions to virtually all important topics of theoretical and applied statistics, and many results bear his name. He has been Editor of Sankhya and theJournal of Multivariate Analysis, and serves on international advisory boards of several professional journals, includingEnvironmetrics and theJournal of Environmental Statistics. This paper is based on the keynote address to the Seventh Annual Conference on Statistics of the United States Environmental Protection Agency.     

A Markov random field spatio-temporal analysis of ocean temperature

The National Oceanic Data Center (NODC) contains historical records from approximately 144,000 hydrographic stations in the North Atlantic. This data has been used by oceanographers to construct maps of point estimates of pressure, temperature, salinity and oxygen in the North Atlantic (Levitus (1994); Lozier et al. (1995)). Because data from any particular year are scarce, the previous maps have been for time-averaged values only. In addition, the maps have been reported without uncertainty estimates. This paper presents a Markov random field (MRF) analysis that can generate maps for specific time periods along with associated uncertainties. To estimate changes in oceanic properties over time previous oceanographic work has focused on differences between a few time periods each having many observations. Due to data scarcity this poses a severe restriction for both spatial and temporal coverage of climatic change. The MRF analysis provides a means for temporal modeling that does not require high data density at each time period. To demonstrate the usefulness of a MRF analysis of oceanic data we investigate the temporal variability along 24.5°N in the North Atlantic. Our results are compared to an earlier analysis (Parrilla et al. (1994)) where data from only three time periods was used. We obtain a more thorough understanding of the temperature change found by this previous study.     

Bayesian model-based cluster analysis for predicting macrofaunal communities

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.