Although causal evidence synthesis is critical for the policy sciences—whether it be analysis for policy or analysis of policy—its repeatable, systematic, and transparent execution remains challenging due to the growing volume, variety, and velocity of policy-relevant evidence generation as well as the complex web of relationships within which policies are usually situated. To address these shortcomings, we develop a novel, semi-automated approach to synthesizing causal evidence from policy-relevant documents. Specifically, we propose the use of natural language processing (NLP) for the extraction of causal evidence and subsequent homogenization of the text; causal mapping for the collation, visualization, and summarization of complex interdependencies within the policy system; and graph analytics for further investigation of the structure and dynamics of the causal map. We illustrate this approach by applying it to a collection of 28 articles on the emissions trading scheme (ETS), a policy instrument of increasing importance for climate change mitigation. In all, we find 300 variables and 284 cause-effect pairs in our input dataset (consisting of 4524 sentences), which are reduced to 70 unique variables and 119 cause-effect pairs after homogenization. We create a causal map depicting these relationships and analyze it to demonstrate the perspectives and policy-relevant insights that can be obtained. We compare these with select manually conducted, previous meta-reviews of the policy instrument, and find them to be not only broadly consistent but also complementary. We conclude that, despite remaining limitations, this approach can help synthesize causal evidence for policy analysis, policy making, and policy research.

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Understanding what conditions promote or hinder energy affordability in energy transitions is crucial for coal and carbon-intensive regions (CCIRs) dealing with the trade-off between phasing out fossil fuels and deepening social inequalities. While previous studies have included household and national-level conditions, this paper addresses the research gap covering regional-level conditions by drawing from regional energy governance, energy justice, and sociotechnical transition frameworks. A mixed-method approach consisting of a fuzzy-set qualitative comparative analysis and case-study analysis is applied to 14 CCIRs in Europe, Asia, and North America. Results show that energy affordability in CCIRs is influenced by combinations of regional and (inter)national conditions. Whereas the existing literature and transition policies do not differentiate between the CCI sector's transition type, this paper highlights that conditions underlying energy (un)affordability differ when the CCI sector is phased out or has the option to transition. Based on the findings, this study calls for a multi-level governance approach to alleviating and preventing energy unaffordability and recommends that policy mixes like the EU Just Transition Fund consider the different types of CCIR transitions.

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Research on the economic burden of air pollution has focused primarily on its macroeconomic impact. However, as some studies have found that air pollution can lead to avoidance behavior–for example, reducing the time spent outdoors–we hypothesize that it can also influence consumer spending activity. We combine high frequency data on ozone and fine particulate pollution with daily consumer spending in brick-and-mortar retail in 129 postal codes in Spain during 2014 to estimate the association between the two. Using a linear fixed effects model, we find that a 1-standard deviation increase in ozone concentration (20.97 μg/m3) is associated with 3.9 percent decrease in consumer spending (95% CI: -0.066, -0.012; p<0.01). The association of fine particulate matter with consumer spending is, however, not statistically significant (β: 0.005; 95% CI: -0.009, 0.018; p>0.10). Further, we do not observe a sufficiently strong bounce-back in consumer spending in the day–or even the week–following higher ozone concentration. Also, we find that the relationship between ozone concentration and consumer spending is heterogeneous, with those aged below 25 and those aged 45 or above exhibiting stronger negative association. This research informs policymakers about a plausibly unaccounted cost of ambient air pollution, even at concentrations lower than the WHO air quality guideline for short-term exposure.@en

Introduction: The target of universal access to affordable, reliable, and modern energy services—key for individual, social, and economic well-being—is unlikely to be achieved by 2030 based on the current trend. Public policy will likely need to play a key role in accelerating progress in this regard. Although perspectives from the field of policy studies can support this effort, to what extent they have been employed in the literature on energy access remains unclear. Methods: This study analyzed nearly 7,500 publications on energy access through a combination of bibliometric review and computational text analysis of their titles and abstracts to examine whether and how they have engaged with public policy perspectives, specifically, policy process research, policy design studies, and the literature on policy evaluation. Results: We discovered 27 themes in the literature on energy access, but public policy was not among them. Subsequently, we identified 23 themes in a new analysis of the 1,751 publications in our original dataset, mentioning “policy” in their title or abstract. However, few of them engaged with public policy, and even those that did comprised a rather small share of the literature. Finally, we extracted phrases pertaining to public policy in this reduced dataset, but found limited mention of terms related to the policy process, policy design, or policy evaluation. Discussion: While to some extent this might reflect the multidisciplinary nature of the research on energy access, a manual review of the abstracts of select publications corroborated this finding. Also, it shed light on how the literature has engaged with public policy and helped identify opportunities for broadening and deepening policy relevant research on energy access. We conclude that, despite their relevance to energy access, public policy perspectives have infrequently and unevenly informed existing research on the topic, and call on scholars in both communities to address this gap in the future.@en

Electricity access is an important issue and building capacity for it requires drawing relevant lessons from past policies. In this study, we evaluate the effect of the Jyotigram Yojana, or the lighted village scheme, a supply-side policy intervention during 2003–08 to increase rural electricity access in Gujarat, India. We hypothesize that policy implementation is associated with increased electricity consumption. To test this hypothesis, we exploit variation in the timing of policy implementation at the village level, and use a generalized difference-in-differences strategy for identification. Further, we use night-time luminosity measured through remote sensing as a proxy for electricity consumption, and control for weather, village fixed effect, year fixed effect, and village or administrative block specific time trend. We find that while the overall effect of the policy on night-time luminosity was not statistically significant, the effects were likely heterogeneous, with the night-time luminosity increasing in some districts after policy implementation and decreasing in others. We conclude that the policy might have had a re-distributive effect on electricity access or consumption and recommend adopting a more holistic approach – incorporating both supply-side and demand-side measures – to increase electricity access.@en

This chapter examines whether and how the difference in the location of evaluators influences the choice of approaches, tools, and procedural issues in policy evaluation. It illustrates the differences in these dimensions of evaluation through a discussion of external and internal evaluation in the case of the Asian Development Bank and multilateral development aid. External evaluators may have a broader perspective and independence, but internal evaluators may have higher-information processing capacity and access to specialized information and records, which external evaluators may not. Impact evaluation is primarily concerned with causal attribution of changes in outcomes to the program or policy. While internal evaluators might be more interested in specific cases, external evaluators often use such techniques to generalize across cases.@en

As policy innovation is essential for upscaling responsible innovation, understanding its relationship to value change(s) occurring or sought in sociotechnical systems is imperative. In this study, we ask: what are the different types of values in the policy process? And, how does value change influence policy innovation? We propose a disaggregation of values and value change based on a four-stream variant of the multiple streams framework (MSF), a conceptual lens increasingly used for explaining policy innovation in sociotechnical transitions. Specifically, we posit that the values that ‘govern’ problem framing, policy design, political decision making, and technological diffusion can evolve relatively independently, potentially leading to value conflict. We apply this framework to the ongoing case of the market-based economic dispatch of electricity (MBED) policy in the Indian energy transition using content analysis. We find that the MBED scheme—with its emphasis on efficiency (problem), economic principles (policy), low-cost dispatch (technology), and centralization (politics)—attempts value change in each stream. Each instance of value change is, however, widely contested, with the ensuing value conflicts resulting in significant opposition to this policy innovation. We conclude that a disaggregation of values based on the MSF can facilitate an analysis of value change and value conflict in sociotechnical transitions and lay the foundation for systematically studying the relationships among technological change, value change, and policy change.@en

A transition to sustainable energy will require not only technological diffusion and behavioral change, but also policy innovation. While research on energy transitions has generated an extensive literature, the extent to which it has used the policy innovation perspective – entailing policy entrepreneurship or invention, policy diffusion, and policy success – remains unclear. This study analyzes over 8000 publications on energy transitions through a bibliometric review and computational text analysis to create an overview of the scholarship, map conceptual engagement with public policy, and identify the use of the policy innovation lens in the literature. We find that: (i) though the importance of public policy is frequently highlighted in the research, the public policy itself is analyzed only occasionally; (ii) studies focusing on public policy have primarily engaged with the concepts of policy mixes, policy change, and policy process; and (iii) the notions of policy entrepreneurship or invention, policy diffusion, and policy success are hardly employed to understand the sources, speed, spread, or successes of energy transitions. We conclude that the value of the policy innovation lens for energy transitions research remains untapped and propose avenues for scholars to harness this potential.@en

While the emergence of big data creates several opportunities for governance and public policy, new capabilities, processes, standards, strategies, and technologies will be essential for deriving value from big data while addressing issues and mitigating risks posed by it. Arguably, above all, these will require the integration of big data analytics in public policy pedagogy. In this chapter, we examine whether public policy education has kept pace with the growth of big data research in the field, with a specific focus on the Asian context. We start by identifying prominent themes in research on big data in governance and public policy. We supplement that with an analysis of academia’s response by examining the prevalence of big data teaching in public policy programs. Based on our analysis, we highlight the current geographic and institutional dominance in research and teaching on the topic, and conclude with key lessons for the design, content, and delivery of pedagogy on big data in public policy.@en

Although cities have risen to prominence as climate actors, emissions’ data scarcity has been the primary challenge to evaluating their performance. Here we develop a scalable, replicable machine learning approach for evaluating the mitigation performance for nearly all local administrative areas in Europe from 2001-2018. By combining publicly available, spatially explicit environmental and socio-economic data with self-reported emissions data from European cities, we predict annual carbon dioxide emissions to explore trends in city-scale mitigation performance. We find that European cities participating in transnational climate initiatives have likely decreased emissions since 2001, with slightly more than half likely to have achieved their 2020 emissions reduction target. Cities who report emissions data are more likely to have achieved greater reductions than those who fail to report any data. Despite its limitations, our model provides a replicable, scalable starting point for understanding city-level climate emissions mitigation performance.@en

Why and how the regulation of emerging technologies occurs is not clear in the literature. In this study, we adapt the multiple streams framework – often used for explaining agenda-setting and policy adoption – to examine the phenomenon. We hypothesize how technological change affects policy-making and identify conditions under which the streams can be (de-)coupled. We trace the formulation of the General Data Protection Regulation to show that the regulation occupied the legislative agenda when a policy window was exploited through policy entrepreneurship to frame technological change as a problem for data privacy and legislative harmonization within the European Union. Although constituencies interested in promoting internet technologies made every effort to stall the regulation, various actors, activities, and events helped the streams remain coupled, eventually leading to its adoption. We conclude that the alignment of problem, policy, politics, and technology – through policy entrepreneurship – influences the timing and design of technology regulation.

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Although India has made significant progress towards the sustainable development goal on energy (SDG 7), further policy innovations are essential for closing the gap, addressing geographic disparities, and harnessing energy for transformative change. Research can support this process by creating policy-relevant knowledge regarding the energy transition, but there is no systematic account of the literature pertaining to energy policy in India to map the research area and suggest key avenues for future research. In this study, I conduct a bibliometric review and computational text analysis of over 2700 publications to identify the key themes, geographies, and public policy concepts (not) examined in the research on energy policy in India. I find that: (i) the literature is dominated by topics in energy supply and less attention is paid to demand-side management, energy efficiency, and electricity distribution; (ii) existing studies have hardly examined subnational policy (-making), especially in the case of eastern and north-eastern India; and (iii) research on both analysis for policy and analysis of policy is limited. I conclude that the current foci lack the breadth and depth necessary for supporting the Indian energy transition and urge scholars to diversify the thematic, geographic, and conceptual engagement in future research@en

Although the emergence of Big Data provides an opportunity to synthesize and mobilize ever greater amounts of policy-relevant knowledge, it has not received adequate attention in studies of policy pedagogy and practice. In this chapter, we highlight the relevance of Big Data to policy analysis, policy implementation, and policy studies through a discussion of basic machine learning techniques and an illustration of their application in the case of better understanding policy response to COVID-19. Subsequently – based on a bibliometric review of nearly 2,500 publications on big data in public policy and content analysis of course titles and descriptions in 122 programs worldwide – we make an evidence-informed appeal to increase the uptake of big data in policy research as well as teaching. We conclude that appropriate engagement with the big data phenomenon can help the policy sciences remain relevant and move a step closer to integrating policy research, pedagogy, and practice.@en

Although the diffusion of policy innovations can promote sustainability, how this process can be accelerated remains unclear. I address this gap by conceptualizing policy transfer and policy diffusion using the multiple streams framework (MSF) and developing hypotheses to connect them. I apply this theorization to explain the limited spread of the energy conservation building code (ECBC) in India by combining a process trace of policy adoption in the embedded case of Andhra Pradesh with a dyadic event history analysis of state-level diffusion during 2012-18. The data for this study are collated from official statistics, elite interviews, news reports, policy documents, and secondary literature. The qualitative analysis shows that policy transfer to Andhra Pradesh occurred when external influence and prior adoption elsewhere were used by a policy entrepreneur to exploit a window of opportunity and couple problem, policy, and politics in the state; the dyadic analysis demonstrates that policy diffusion was influenced by the interaction among the structural characteristics of the problem, policy, and politics streams. I conclude with the implications for research on policy diffusion and the MSF as well as the relevance of the findings for promoting policy innovation for a sustainable energy transition.

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Energy democracy is an emergent movement for bringing about technological innovation with concomitant economic, social, and political change in the energy system, one that promises a just transition to clean and affordable energy. To examine whether and how the promise of energy democracy can be fulfilled, however, more clarity on the concept is necessary. In this chapter, we conceptualize energy democracy using a four-stream variant of the multiple streams framework. From this perspective, sustainability transitions and, by extension, energy transitions can be viewed as multilevel, multiactor processes requiring not only technological innovation but also problem framing, policy alternatives’ specification, and political advocacy. The actors primarily responsible for these transition processes are technology constituencies, epistemic communities, instrument constituencies, and advocacy coalitions, respectively. Energy democracy can be conceived as public participation in these activities during transition processes. Further, as this conceptualization can be operationalized at every scale, the cross-scalar nature of energy transitions—and energy democracy—can be analyzed by modeling the influence of transition processes in one setting on the streams in another setting. The proposed framework furthers inquiry into energy democracy through examination of the what, the how, and the where of democratization in society.@en

While the emergence of big data raises concerns regarding governance and public policy, it also creates opportunities for diversifying the toolkit for analysis for the policy sciences as a whole, i.e., research concerning policy analysis as well as policy studies. Further, it opens avenues for practice, which together with research requires adaptation in teaching curricula if policy education were to remain relevant. However, it is not clear to what extent this opportunity is being realized in public policy research and teaching. In this study, we examine the prevalence of big data analytics in public policy research and pedagogy using bibliometric analysis and topic modeling for the former, and content analysis of course titles and descriptions for the latter. We find that despite significant scope for application of various big data techniques, the use of these analytic techniques in public policy has been largely limited to select institutions in a few countries. Further, data science has received limited attention in policy pedagogy, once again with significant geographic variation in its prevalence. We conclude that, to stay relevant, the policy sciences need to pay more attention to the integration of big data techniques in policy research, pedagogy, and thereby practice.

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Although understanding initial responses to a crisis such as COVID-19 is important, existing research on the topic has not been systematically comparative. This study uses topic modeling to inductively analyze over 13,000 COVID-19 policies worldwide. This technique enables the COVID-19 policy mixes to be characterized and their cross-country variation to be compared. Significant variation was found in the intensity, density, and balance of policy mixes adopted across countries, over time, and by level of government. This study advances research on policy responses to the pandemic, specifically, and the operationalization of policy mixes, more generally.

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Although many scholars have studied policy success and failure, the relationship of these phenomena to the policy process—essential for an explanatory or anticipatory analysis—remains unclear. I address this gap by linking the policy success heuristic with the multiple streams framework (MSF) and developing hypotheses to explain outcomes. I apply this conceptualization to the case of the solar energy policy in Gujarat, India by combining a qualitative policy assessment with a process trace of policy making. The findings show that the conflicted process success and the programmatic failure resulted largely from a top-down push for a policy without a problem. This push, nevertheless, led to political success that was sustained by recoupling problems and politics through agenda denial, blame avoidance, and credit claiming. I conclude with implications for the research on policy evaluation, the MSF, and policy studies, as well as the governance of a sustainable energy transition.@en

A growing body of research indicates that in-utero exposure to ambient fine particulate matter (PM2.5) is a risk factor for low birth weight (LBW). However, research on India, where the high and rising level of ambient air pollution is a significant health concern, is limited. In this study, we analyze the association between ambient PM2.5 and LBW in India. We match data on birth weight from the National Family and Health Survey (NFHS) conducted in India in 2015-16 with high-resolution spatial data on annual ambient PM2.5 concentration to calculate in-utero exposure based on the residential location of each child. We estimate the association of in-utero exposure to ambient PM2.5 with birth weight and LBW, after adjusting for child level, maternal, and household level characteristics that predict birth weight. In our sample (n = 149 416), in comparison to the reference category of in-utero PM2.5 level less than 26.7 µg m-3, the adjusted OR of LBW increases non-linearly from 1.098 (95% CI: 0.954, 1.263) for children in the exposure band 39.3-44.7 µg m-3 (i.e., the fourth octile) to 1.241 (95% CI: 1.065, 1.447) for those in the exposure band 44.7-51.6 µg m-3 (i.e., the fifth octile) and 1.405 (95% CI: 1.126,1.753) for those with in-utero PM2.5 level greater than 77.3 µg m-3 (i.e., the last octile). Our findings show that exposure to ambient PM2.5 is strongly associated with LBW in India and suggest that policies that improve air quality may be necessary for achieving the World Health Assembly target of 30% reduction in LBW by 2025.

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Although learning in networks is key to transitions, the literature has juxtaposed the different actors involved. Based on a conceptual review, we posit that four distinct collective actors engage in learning in transitions: technology constituencies, epistemic communities, instrument constituencies, and advocacy coalitions. Technology constituencies promote the adoption of specific socio-technical alternatives by citizens, businesses, or governments. Epistemic communities shed light on sustainability issues at the science-policy interface. Instrument constituencies promote the creation and diffusion of specific governance arrangements. Advocacy coalitions compete politically to implement their policy preferences. These collective actors are responsible for facilitating technological learning, sustainability problem learning, policy instrumental learning, and political learning, respectively, which are in turn necessary for niche development, regime destabilization, overcoming path dependence, and political change. Consequently, the alignment of these distinct, albeit potentially overlapping, collective actors can have a profound impact on learning in transitions and the emergence of transition pathways.

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