Value Change, Value Conflict, and Policy Innovation: Understanding the Opposition to the Market-Based Economic Dispatch of Electricity Scheme in India Using the Multiple Streams Framework.

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.

Ambient air pollution and consumer spending: Evidence from Spain.

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 (ß: 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.

Predicting European cities' climate mitigation performance using machine learning.

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.

Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies.

Climate change has been called "the defining challenge of our age" and yet the global community lacks adequate information to understand whether actions to address it are succeeding or failing to mitigate it. The emergence of technologies such as earth observation (EO) and Internet-of-Things (IoT) promises to provide new advances in data collection for monitoring climate change mitigation, particularly where traditional means of data exploration and analysis, such as government-led statistical census efforts, are costly and time consuming. In this review article, we examine the extent to which digital data technologies, such as EO (e.g., remote sensing satellites, unmanned aerial vehicles or UAVs, generally from space) and IoT (e.g., smart meters, sensors, and actuators, generally from the ground) can address existing gaps that impede efforts to evaluate progress toward global climate change mitigation. We argue that there is underexplored potential for EO and IoT to advance large-scale data generation that can be translated to improve climate change data collection. Finally, we discuss how a system employing digital data collection technologies could leverage advances in distributed ledger technologies to address concerns of transparency, privacy, and data governance.

Mobilizing Policy (In)Capacity to Fight COVID-19: Understanding Variations in State Responses.

The objective of this collection of essays is to gain insights into the different national-level state responses to COVID-19 around the world and the conditions that shaped them. The pandemic offers a natural experiment wherein the policy problem governments faced was the same but the responses they made were different, creating opportunities for comparison of both the kinds of policy tools being used and the factors that accounted for their choice. Accordingly, after surveying on-line databases of policy tools used in the pandemic and subjecting these to topic modelling to reveal the characteristics of a 'standard' national pandemic response, we discuss the similarities and differences found in specific responses. This is done with reference to the nature and level of policy capacity of respective governments, highlighting the critical roles played by (in)adequate preparation and lesson-drawing from past experiences with similar outbreaks or crises. Taken together the articles show how the national responses to the COVID-19 pandemic were shaped by the opportunity and capacity each government had to learn from previous pandemics and their capacity to operationalize and build political support for the standard portfolio of policy measures deployed to deal with the crisis. However, they also show how other factors such as the nature of national leadership, the organization of government and civil society, and blindspots towards the vulnerabilities of certain population segments also helped to shape policy responses to the pandemic.

Early-life exposure to ambient fine particulate air pollution and infant mortality: pooled evidence from 43 low- and middle-income countries.

BACKGROUND: Many low- and middle-income countries are experiencing high and increasing exposure to ambient fine particulate air pollution (PM2.5). The effect of PM2.5 on infant and child mortality is usually modelled using concentration response curves extrapolated from studies conducted in settings with low ambient air pollution, which may not capture its full effect. METHODS: We pool data on more than half a million births from 69 nationally representative Demographic and Health Surveys that were conducted in 43 low- and middle-income countries between 1998 and 2014, and we calculate early-life exposure (exposure in utero and post partum) to ambient PM2.5 using high-resolution calibrated satellite data matched to the child's place of residence. We estimate the association between the log of early-life PM2.5 exposure, both overall and separated by type, and the odds of neonatal and infant mortality, adjusting for child-level, parent-level and household-level characteristics. RESULTS: We find little evidence that early-life exposure to overall PM2.5 is associated with higher odds of mortality relative to low exposure to PM2.5. However, about half of PM2.5 is naturally occurring dust and sea-salt whereas half is from other sources, comprising mainly carbon-based compounds, which are mostly due to human activity. We find a very strong association between exposure to carbonaceous PM2.5 and infant mortality, particularly neonatal mortality, i.e. mortality in the first 28 days after birth. We estimate that, at the mean level of exposure in the sample to carbonaceous PM2.5-10.9 µg/m3-the odds of neonatal mortality are over 50% higher than in the absence of pollution. CONCLUSION: Our results suggest that the current World Health Organization guideline of limiting the overall ambient PM2.5 level to less than 10 µg/m³ should be augmented with a lower limit for harmful carbonaceous PM2.5.

Exposure to Ambient Fine Particulate Air Pollution in Utero as a Risk Factor for Child Stunting in Bangladesh.

Pregnant mothers in Bangladesh are exposed to very high and worsening levels of ambient air pollution. Maternal exposure to fine particulate matter has been associated with low birth weight at much lower levels of exposure, leading us to suspect the potentially large effects of air pollution on stunting in children in Bangladesh. We estimate the relationship between exposure to air pollution in utero and child stunting by pooling outcome data from four waves of the nationally representative Bangladesh Demographic and Health Survey conducted between 2004 and 2014, and calculating children's exposure to ambient fine particulate matter in utero using high resolution satellite data. We find significant increases in the relative risk of child stunting, wasting, and underweight with higher levels of in utero exposure to air pollution, after controlling for other factors that have been found to contribute to child anthropometric failure. We estimate the relative risk of stunting in the second, third, and fourth quartiles of exposure as 1.074 (95% confidence interval: 1.014-1.138), 1.150 (95% confidence interval: 1.069-1.237, and 1.132 (95% confidence interval: 1.031-1.243), respectively. Over half of all children in Bangladesh in our sample were exposed to an annual ambient fine particulate matter level in excess of 46 µg/m³; these children had a relative risk of stunting over 1.13 times that of children in the lowest quartile of exposure. Reducing air pollution in Bangladesh could significantly contribute to the Sustainable Development Goal of reducing child stunting.