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      The global expansion of climate mitigation policy interventions, the Talanoa Dialogue and the role of behavioural insights

      , , , ,
      Environmental Research Communications
      IOP Publishing

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          Abstract

          Increasing attention is being paid to the Paris Climate Agreement and the impacts of Nationally Determined Contributions (NDCs) intended to limit global warming to 1.5 °C. However, the nature and evolution of existing policy mixes that underlie NDCs remain poorly understood. This critical issue has emerged from the outcomes of the Talanoa Dialogue for Climate Ambition, where little progress was made in building a comprehensive, evidence-based foundation for effective climate policy. To a large extent, this is due to the nature of the process, and a lack of data related to policy interventions in the pre-2020 period, notably their composition, coverage, and orientation. We seek to address these shortcomings by applying a directed content analysis to a dataset of national and city-level policy interventions. The aim is to quantify the nature and evolution of policy efforts that promote the adoption of low-carbon energy technologies (LCETs) globally. Fifteen databases, containing more than 10,000 policies and measures were reviewed. Our findings highlight the rapid spread of policy portfolios and an international convergence towards economic incentives (notably subsidies). At the city level, technology and infrastructure policies dominate. However, it is unclear to what extent behavioural factors (i.e., cognitive, motivational and contextual aspects) that affect the choice and use of LCETs are taken into account in policy design. This is particularly important because studies that model the feasibility of the 1.5 °C target reveal behavioural changes and the rapid adoption of low-carbon lifestyles as critical enabling factors. In response to the outcomes from the Talanoa Dialogue, we argue that policymakers need to go beyond stringent policy mixes and rapidly re-think their traditional economic-driven policymaking approach. Far more attention needs to be given to behavioural factors when designing, evaluating and implementing LCET policies.

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          Three approaches to qualitative content analysis.

          Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.
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            The qualitative content analysis process.

            This paper is a description of inductive and deductive content analysis. Content analysis is a method that may be used with either qualitative or quantitative data and in an inductive or deductive way. Qualitative content analysis is commonly used in nursing studies but little has been published on the analysis process and many research books generally only provide a short description of this method. When using content analysis, the aim was to build a model to describe the phenomenon in a conceptual form. Both inductive and deductive analysis processes are represented as three main phases: preparation, organizing and reporting. The preparation phase is similar in both approaches. The concepts are derived from the data in inductive content analysis. Deductive content analysis is used when the structure of analysis is operationalized on the basis of previous knowledge. Inductive content analysis is used in cases where there are no previous studies dealing with the phenomenon or when it is fragmented. A deductive approach is useful if the general aim was to test a previous theory in a different situation or to compare categories at different time periods.
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              Paris Agreement climate proposals need a boost to keep warming well below 2 °C.

              The Paris climate agreement aims at holding global warming to well below 2 degrees Celsius and to "pursue efforts" to limit it to 1.5 degrees Celsius. To accomplish this, countries have submitted Intended Nationally Determined Contributions (INDCs) outlining their post-2020 climate action. Here we assess the effect of current INDCs on reducing aggregate greenhouse gas emissions, its implications for achieving the temperature objective of the Paris climate agreement, and potential options for overachievement. The INDCs collectively lower greenhouse gas emissions compared to where current policies stand, but still imply a median warming of 2.6-3.1 degrees Celsius by 2100. More can be achieved, because the agreement stipulates that targets for reducing greenhouse gas emissions are strengthened over time, both in ambition and scope. Substantial enhancement or over-delivery on current INDCs by additional national, sub-national and non-state actions is required to maintain a reasonable chance of meeting the target of keeping warming well below 2 degrees Celsius.
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                Author and article information

                Contributors
                Journal
                Environmental Research Communications
                Environ. Res. Commun.
                IOP Publishing
                2515-7620
                June 17 2019
                July 01 2019
                June 17 2019
                July 01 2019
                : 1
                : 6
                : 061001
                Article
                10.1088/2515-7620/ab26d6
                0c08314d-a39a-46c8-8cca-771ee880f0c4
                © 2019

                http://creativecommons.org/licenses/by/3.0/

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