21 research outputs found

    Measuring Environmental Action and Economic Performance in Developing Countries

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    Significant advances have been made in measuring the stringency of environmental policies, and understanding the relationship between environmental action and economic dynamics, particularly in high-income countries. Despite this, unequivocal empirical evidence on the impact of environmental policies on economic performance remains elusive, with conclusions being highly dependent on the conceptual and methodological choices with respect to defining and measuring the stringency of environmental policies. Most importantly, the literature evaluating these issues in developing countries remains sparse and robust findings are even more difficult to extract. This study reviews the existing body of work in both developed, and, where available, developing countries. It provides a comprehensive assessment of how environmental policy stringency has been measured, outlining definitional and conceptual challenges. It discusses the advantages and disadvantages of different indicators, and their usefulness for application in developing countries. In an effort to improve our understanding of the impact of environmental policy stringency in middleand low-income countries, the study draws lessons for the prioritization of future data collection and measurement efforts. Through the study, two types of stringency indicators emerge as requiring the most attention: de facto enforcement indicators and de jure explicit measures that capture the stringency of specific environmental laws, rules and regulations. While there is no “best” conceivable measure of the stringency of environmental policies, a multidimensional approach to quantifying stringency in developing countries, with a focus on explicit direct measures, is advocated. Data collection and indicator-improvement efforts need, though, to be updated periodically and supplemented by other proxies for environmental stringency

    An Inquiry into Model Validity When Addressing Complex Sustainability Challenges

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    Scientific modelling is a prime means to generate understanding and provide much-needed information to support public decision-making in the fluid area of sustainability. A growing, diverse sustainability modelling literature, however, does not readily lend itself to standard validation procedures, which are typically rooted in the positivist principles of empirical verification and predictive success. Yet, to be useful to decision-makers, models, including their outputs and the processes through which they are established must be, and must be seen to be “valid.” This study explores what model validity means in a problem space with increasingly interlinked and fast-moving challenges. We examine validation perspectives through ontological, epistemic, and methodological lenses, for a range of modelling approaches that can be considered as “complexity-compatible.” The worldview taken in complexity-compatible modelling departs from the more standard modelling assumptions of complete objectivity and full predictability. Drawing on different insights from complexity science, systems thinking, economics, and mathematics, we suggest a ten-dimensional framework for progressing on model validity when investigating sustainability concerns. As such, we develop a widened view of the meaning of model validity for sustainability. It includes (i) acknowledging that several facets of validation are critical for the successful modelling of the sustainability of complex systems; (ii) tackling the thorny issues of uncertainty, subjectivity, and unpredictability; (iii) exploring the realism of model assumptions and mechanisms; (iv) embracing the role of stakeholder engagement and scrutiny throughout the modelling process; and (v) considering model purpose when assessing model validity. We wish to widen the debate on the meaning of model validity in a constructive way. We conclude that consideration of all these elements is necessary to enable sustainability models to support, more effectively, decision-making for complex interdependent systems
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