Milind Kandlikar

Predicting crop yield response to climate change is a topic of active research. A popular method involves building statistical models using historical climate and agricultural data, and then applying them on future climate projections for predicting crop yields. Using India as a case study, this dissertation examines these statistical models along two dimensions: the type of climate variables included, and the statistical techniques used. We also employ these models for predicting climate change impact on Indian crop yields till 2100.First, we examine the role of seasonal (e.g. total seasonal precipitation) versus subseasonal (e.g. precipitation over each crop growing stage) climate variables in explaining crop yields. We observe that even though adding extra climate variables does not always improve overall model accuracy, the proportion of yield variability explained by climate (versus non-climatic variables like geography and time) can increase significantly. This underscores the importance of combining physiological and statistical knowledge while choosing climate variables for statistical crop models. Second, we compare the well-known statistical method of OLS linear regression (LR) to a popular machine learning method called boosted regression trees (BRTs). While LR models were simpler to interpret, BRTs could uncover unexpected non-linear relationships and exhibited better yield prediction accuracy. Compared to LR, BRTs sometimes showed lower sensitivity to temperature variation. Higher flexibility of BRTs allowed them to identify obscure interactions between variables that could be missed by LR.We then use different climate variables and statistical techniques for building statistical models to predict climate change impact on India’s future crop yields. We found that nationally-averaged rice, wheat, and pearl millet yields could reduce by up to 3.4, 4.3, and 5.5 percent (respectively) by 2050 under the intermediate emissions scenario. Some parts of India may benefit from climate change, while other regions could face yield losses of up to 20 percent. Depending on the climate variables or statistical technique employed, we observe high variability in yield change predictions. We therefore suggest combining multiple models for estimating climate change impact on crop yields.

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One of the most damaging consequences of forest management and wildlife conservation policies around the world has been their pivotal role in the long-term dispossession of Indigenous groups from their ancestral lands. Indigenous presence in, knowledge, and understanding of the natural world is perceived as a problem requiring the correction and intervention of the state. These wrongful assumptions are dominant in the treatment of Adivasi (India's Indigenous people) across post-colonial India. This dissertation empirically investigates the relationship of Kattunayakans, a hunter-forager Adivasi community of Southern India and protected area forest landscapes. It critically contrasts the ideology that defines India's forest policy with Adivasi views of human relationships with wildlife, forested land, forest fire, and forest food.From all the above, chapter 2 characterizes Kattunayakan ways of engagement with wildlife as a form of 'deep coexistence' that describes wild animals as: rational beings in conversation with humans; as gods, teachers, and equals; and as relatives with shared origins practicing dharmam (alms). Chapter 3 contributes empirical evidence to the study of Adivasi-forest relationships by articulating socio-cultural meanings and values that Kattunayakans associate with protected area forests. Chapter 4 engages with Adivasi knowledge of forest fire as an agent, co-manager, actor, preserver, groomer, and enabler of socio-ecological functions. It contests the notion of a forest fire as a dangerous phenomenon that should be quickly extinguished and positions fire as a co-habitant being, on par with animal and human residents. Chapter 5 seeks to expand understanding of Adivasi food transitions and ensuing consequences for the socio-ecology of Indigenous peoples. It describes the food as a facilitator of knowledge, memories, identities, aspirations, reciprocities, relationships, and ways of living. It highlights the need to learn about Adivasi foodways beyond nutrition and have policies that bring an Adivasi inclusive take on food transitions. What emerges is an interpretation of the forest that emphasizes coexistence over domination, highlighting the fluid agency of animal and non-animal entities over rigid policy prescriptions and broader notions of forest security as human security. Together these views remain central to Adivasi well-being despite decades of forced dislocation.

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The majority of the Indian population relies on traditional fuels such as biomass, and an estimated million people die prematurely in India due to poor air quality, both indoor and outdoor, annually. A major CO₂-emitter, India has also committed to a low carbon development pathway. In this thesis, I study the problem of indoor and outdoor air pollution exposure in India, and its links to energy equity and climate mitigation.First, I use national-scale household survey data to quantify changes in cooking habits and health. I show that electrification and complete transition to Liquefied Petroleum Gas (LPG) provide significant health and time-saving benefits, particularly for women, but using LPG together with biomass provides negligible benefits. Second, I use national-scale household energy use data to show that indoor exposures to fine particulate matter (PM2.5) in rural and low-income urban households exceed those in higher income urban households by an order of magnitude. Using a diffusion model for future energy use I show that comprehensive access to modern fuels for all is needed to drastically reduce indoor PM2.5 exposures, and it would add minimally to current levels of GHG emissions, if non-Kyoto pollutants from traditional fuels are considered. Third, I construct a multi-box atmospheric transport model for PM2.5 to analyze the spatial and seasonal variation of ambient air pollution. I then use a national air pollution inventory to calculate source contribution to ambient PM2.5 exposure. Residential biomass use dominates mortality burdens from ambient air pollution in India. I show that the informal sector (sources beyond the direct ambit of regulation including traditional household fuels) is responsible for 73% of deaths attributable to ambient air pollution and 33% of nation-wide GHG (including non-Kyoto) emissions. Coal use in formal industry is the second leading contributor to mortality and is responsible for half of national GHG emissions. The significant contribution of informal sources to ambient air pollution and GHG emissions leads to the conclusion that a focus on formal sources (e.g. power and transport) alone is inadequate - the role of informal sources needs to be addressed to meet air quality and climate goals.

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Paratransit plays a vital role in cities in the Global South, including India. Paratransit modes - those which lack a fixed route and timetable - perform trips impractical or impossible on mass transit and, in Delhi, increasingly provide feeder services to the city’s metro rail and Bus Rapid Transit (BRT) systems.Despite their critical role in the urban transit ecosystem in Indian cities, paratransit operators have a poor reputation amongst the middle-classes and the English-language media for overcharging passengers and flouting regulations. In response, the current policy approach to paratransit regulation is marked by judicial and technical interventions. These are seldom empirically driven, and often represent a relative neglect of the paratransit sector. This study aims to correct this neglect by providing a detailed empirical account of paratransit operations and the regulatory environment in Delhi. It makes the case that paratransit is a vital last mile mode, and that evidence based regulation of paratransit modes is critical to improving public transport in Indian cities.This study does this by analyzing existing paratransit regulation and demonstrating that the current approach involves a heavy burden of compliance that inflates costs for operators. It then presents an empirical analysis of the economics of operating an auto-rickshaw using a survey of drivers (n=301). It finds that overcharging may be explained by the structure of operators’ costs and revenues. A survey of passengers (n=689) provides data for a series of statistical models, which suggests that the incidence and magnitude of overcharging are explained by the economics of auto-rickshaw trip making and associated with such variables as trip distance and the location of the destination.It then seeks to explain the rise of battery-rickshaws as a mode paratransit in the city and the lessons provided by their sudden proliferation. A fieldwork survey of drivers (n=302) provides data on their role and operating economics. A passenger survey (n=540) finds battery-rickshaws are largely replacing cycle-rickshaws. This case study sheds light on the environment in which urban transportation policy decisions are made. The study concludes by presenting some policy recommendations for paratransit in Delhi.

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The harmonization of climate-adaptive behaviour with pre-existing decision-making processes is central to the way climate change adaptation is described in the literature. Yet such behaviour is largely understudied, making it difficult to predict whether and how individuals can integrate (i.e., ‘mainstream’) climate change with risk management processes for weather and other ‘normal’ stressors. In this dissertation, I examine the decision-making processes of South Africa’s commercial grain farmers, as a uniquely informative case, through five complementary studies of two original datasets. I seek to better understand the relationship between risk perceptions and climate-adaptive behaviour in this group, who are known to be sensitive to weather risks and who are adopting climate-resilient farming practices (i.e., Conservation Agriculture (CA)), but who are nonetheless perceived by local experts to be insensitive to climate change risks. In doing so, I distinguish between weather-sensitive decision-making, in which farmers perceive and react to weather risks in conjunction with other ‘normal’ risks, and climate-sensitive decision-making, in which they also perceive and react to the anticipated effects of climate change. Using mental models interviews in the Western Cape province (N = 90), I first reconceptualise farmers’ risk-based decision-making processes, drawing on theories of risk perception and framing from cognitive psychology and behavioural economics to interpret the empirical evidence. Second, I explain the variation in farmers’ adoption of climate-resilient CA practices based on the different cognitive frames (expressed as linguistic frames) that they use to perceive, interpret and respond to weather risks. Third, I use these results to guide the quantitative analysis of a national survey (N = 441), with which I assess the utility of the CA concept in promoting, monitoring and evaluating sustainable and climate-resilient farming practices, as envisioned by the Climate-Smart Agriculture and Sustainable Intensification frameworks. Fourth, I use the interview data to evaluate whether and how farmers integrate climate change and weather risks in farm-level decision-making. Fifth, I build on these findings by using the survey data to quantitatively test whether farmers perceive weather and climate change as equivalent risks.

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Urban air pollution is a major health and environmental concern worldwide, and the levels are extremely high in New Delhi, India. This research is focused on the spatial and temporal variability of air pollutant concentrations and its implications for population exposure in New Delhi. Since traffic is considered a significant source of air pollutants in urban environments, robust and multiple linear regression models were used to understand the impact of local traffic flow on ambient concentrations of PM₂.₅, CO, NO and NO₂ at a busy intersection. To elicit the spatiotemporal variability of PM₂.₅ and its constituents (black carbon and ultrafine particles), land use regression (LUR) models were developed. Separate morning and afternoon models were developed using 136 hours (39 sites), 112 hours (26 sites) and 147 hours (39 sites) of PM₂.₅, BC and UFPN data, respectively. Finally, to understand how spatiotemporal variations in PM₂.₅ concentrations impact population exposure, a probabilistic simulation framework was developed to integrate the PM₂.₅ LUR models with time-activity data obtained from a field survey. Regression models explained about 50–80% variability in hourly pollutant concentrations and localized traffic flow explained up to 19% of variability on that scale. Auto-rickshaw and truck flow had a higher influence on NO₂ and PM₂.₅ concentrations, respectively. Independent variables in the LUR models included population density, distance from major roads, and major and minor road lengths in buffers of different radii; measurements from a fixed continuous monitoring site were also used as independent variables in the PM₂.₅ and BC models. The temporal term explained most of the variability (63–77%) in PM₂.₅ and BC models compared to spatial variables (4–16%). Exposure simulations indicate that the estimated annual average PM₂.₅ exposure (109 µg m-³) was high compared to North American or European cities. PM₂.₅ exposures were highest during the winter months (~200 µg m-³) compared to the summer months (~50 µg m-³). Ignoring mobility (i.e. exposure during transport or at work/school locations), as is generally assumed in epidemiologic studies of long-term exposure, underestimated PM₂.₅ population exposure by about 11%.

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The environmental impacts of information and communication technologies and consumer electronicsare challenging to evaluate. Organizations and individuals wishing to reduce the impacts attributable to their usage of these products and systems rely on a limited technical knowledge base that struggles to stay current. Using a life cycle assessment approach which expresses environmental impacts quantitatively in terms of greenhouse gas emissions and primary energy demand, this dissertation significantly expands our understanding of the impacts of desktop computers, electronics products in general, and connected media services accessed in the home, in order to support environmentally-conscious decision-making and policy regarding these products and systems. The first of three studies, a meta-analysis of prior life cycle assessments of desktop PCs, resolves an important ambiguity in this literature and demonstrates that greenhouse gas emissions due to operational energy consumption usually exceed those due to device manufacturing. The second study calculates embodied greenhouse gas emissions of eleven electronics products through a teardown analysis, and finds a linear relationship between mass and embodied emissions, thus demonstrating that lightweight, compact products offer environmental benefits relative to larger products. A comparison to studies of older products also reveals that newer products are more materially efficient, largely due to reduction in integrated circuit content per product. Finally, the third study calculates aggregate US consumer greenhouse gas emissions due to broadcast television, video on demand, online video, other online uses, and offline uses when consumed using televisions, personal computers, tablets, and smartphones, including emissions due to devices in the home, networks, and datacenters. The study concludes that emissions due to video end-uses account for 75% of total consumer ICT emissions. About 71% of consumer ICT emissions arise due to devices in the home, especially TVs and desktop PCs, with the remainder due to networks and datacenters. Mobile platforms using Wi-Fi connections are the least impactful mode of consuming connected media content. Collectively, the dissertation argues for a more integrated approach towards impact estimation, in order to surmount issues regarding variation of modeling assumptions across existing studies, longevity of published work, and coverage of emerging products and services.

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Engineered nanomaterials (ENMs) promise great benefits for society, yet our knowledge of potential risks and best practices for regulation are still in their infancy. High uncertainty and novel ENM properties complicate the management of risk, rendering existing regulatory frameworks inadequate. This thesis investigates the challenges that nanotechnologies pose for risk regulation, and aims to inform the development of policies and practices to address these challenges. In chapter 2, US federal environmental, health and safety (EHS) regulations are analyzed using a life cycle framework, to evaluate their adequacy as applied to ENMs. This analysis reveals that life cycle risk management of nanomaterials under existing regulations is plagued with difficulty, and populated by myriad gaps through which ENM may escape federal oversight altogether. Chapters 3 and 4 examine expert opinions on risks, and perceptions of regulatory agency preparedness to manage risks, using a web-based survey (N=404) of US and Canadian nanotechnology experts. Risk and preparedness perceptions were found to differ significantly across groups of experts. Nano-scientists and engineers were more than twice as likely as nano-regulators to believe that benefits from nanotechnology would greatly exceed risk. Yet, those working in regulatory agencies were far more likely to regard government agencies as unprepared than were experts outside government. These differences were explained by expert views of the novelty of benefits and risks, attitudes toward other classes of risk, preferred approaches to regulation, experts’ degree of economic conservatism, and trust in regulatory agencies.Recognizing the myriad challenges for risk regulation, chapter 5 explores the use of decision-analytic models to cope with uncertainty. Drawing on baseline data monitoring efforts of the US EPA and California DTSC, this chapter argues for the use of novel decision-analytic tools and approaches (such as risk ranking, multi-criteria decision analysis, and “control banding”) in lieu of formal risk assessment to meet regulators’ goals in particular decision contexts.Considered together, this thesis concludes that oversight can be improved through pending regulatory reforms, the utilization of expert opinion to inform decision-making, and the development of improved decision-analytic tools that enable the assessment and management of risks under high uncertainty.

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This dissertation investigates the social controversy and regulatory challenges presented by genetically engineered (GE) crops in India. Current research insufficiently addresses risk controversies in the developing world, nor provides adequate consideration of GE biosafety as an important socio-political concept as well as a technical one. The study addresses these gaps by mapping the GE controversy in India, its insertion into health and safety decision-making, and the ways in which divergent stakeholders have established positions in these risk debates. Secondly, it assesses the challenges facing the biosafety regulatory regime in India, particularly as a country undergoing a "risk transition," whereby a growing middle class and marginal farmers are pitted against one another in surprising ways.The data for this study are drawn from three main sources using a case study methodology. Firstly, interviews with: farmers, civil society groups, and regulators. Secondly, an analysis of key policy and legal documents that serve as the foundation of India's regulatory regime. And finally, an analysis of the literature and materials that help make up the "public debate" including NGO publications, website postings, films, and newspaper articles covering aspects of the controversy from key Indian English language sources. Rooted in the social studies of risk, the dissertation also draws from literatures on comparative policy analysis, narrative theory in social science, political ecology, and science and technology studies. India is undergoing a "risk transition", and the country's response to GE agriculture can be expected to differ from what has been more thoroughly mapped in other parts of the world. Moreover, biosafety is the central organizing principle of agricultural biotechnology regulation in India, and its ongoing negotiated quality has spurred both regulatory innovation and larger governance challenges.India has a diverse, largely agrarian population and this study finds that developing new ways to understand how the GE regulatory regime is changed by public debate is crucial, as are meaningful ways to solicit and incorporate public participation in a complex democracy. Strategies to address public perspectives must extend beyond organized civil society groups to include other citizens, especially marginal producers with much at stake in the GE debates.

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Emissions from on-road transportation sources are a complex mixture of gaseous and particulate pollutants. Particulate matter (PM) emissions are especially important because – although short lived in the atmosphere – they are strongly associated with cardiovascular and respiratory disease and are strong climate forcing agents. The overall objective of this research was to quantify the effectiveness of emission control policies for in-use vehicles in India. I have focused on understanding the impacts of large-scale adoption of compressed natural gas (CNG) as an alternative to diesel and gasoline in New Delhi, India. In Chapter 2, I quantified the climate impacts of switching to CNG for public transportation vehicles (taxis and buses). The study showed that converting buses from diesel to CNG significantly reduced climate-warming diesel particulate matter (PM), but the increase in CH₄ emissions from all vehicle types offset much of this benefit. Chapters 3-5 focused on auto-rickshaws (three-wheeled taxis), which are an important mode of passenger transport in many developing countries. In Chapter 3, a survey of 350 drivers quantified activity patterns, fuel consumption and CO₂ emissions for auto-rickshaws, and a model was developed to better understand the determinants of visible smoke emissions. Chapter 4 describes a laboratory (chassis dynamometer) study that measured emissions from 31 auto-rickshaws, and establishes fuel-based emission factors for gaseous and fine PM pollutants from 2-stroke and 4-stroke spark-ignited engines fueled with CNG and gasoline. Finally, Chapter 5 examines a range of emission-reduction policies for auto-rickshaws, including phasing out 2-stroke engines, switching to CNG fuel, scrapping older vehicles and four different types of inspection and maintenance (I/M) programs. Together, these studies demonstrate that certain fuel/engine combinations, such as CNG-fueled 4-stroke engines, are more robust low-emitters than others, and can be an effective alternative to diesel engines (in buses) or 2-stroke engines (in auto-rickshaws). Although this research has examined emissions-reduction policy in New Delhi, the findings are applicable to in-use vehicles in many other jurisdictions in the developing world.

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