Search

The burning of agricultural residue from previous season’s rice crop is a key contributor to poor air quality during the winter across North India, primarily in the states of Punjab and Haryana. Air quality can deteriorate to catastrophic levels during the Agricultural Waste Burning (AWB) season in October-November, when fine particulate matter (PM2.5) concentrations can exceed WHO daily maxima over a sustained period by an order of magnitude or more, over a large swathe of the Indo-Gangetic plain. Over the past decade, attempts by Indian governments to change farmer behavior by incentivizing the use of novel technologies for managing rice residue without burning it have been met with little success. This paper uses farmer and expert interviews, as well as secondary data, to examine the barriers to adoption of these technologies in the state of Punjab. We analyze how operational factors (such as farm size, timing, technology availability, and choice) affect a farmers’ decision to choose (or not) a rice residue management practice. We develop a financial model for analyzing the costs of residue management technologies that are consistent with the decision-making process of both small and large farmers. We find that more sustainable residue management practices can be cost effective relative to residue burn, especially when existing subsidies are applied. However, difficulties in accessing technological alternatives to AWB and subsidies for their use, and a lack of trust in the government’s ability to deliver the full benefits of subsidies, all contribute the low adoption of technological alternatives to AWB.

Climate services are intended to improve climate-sensitive decisions by making climate information ‘useful, useable and used’. Here, we analyse 27 expert interviews to evaluate whether this user-driven model of climate science has been successfully implemented in the public sector. We show that, although climate services promise better decision-making, they mainly focus on delivering better data. The norms and institutions of climate science produce three key tensions in operationalizing climate services: a focus on products rather than processes, services based on broad assumptions about demand rather than being demand-driven, and the narrow economic valuation of products rather than evaluation of improvements in decision-making. These tensions help explain why climate services often generate nominal changes in climate science where transformations are promised. Transformational change requires that climate services account for diverse social structures, behaviours and contexts. Integrating social science is no panacea for demand-driven climate services, but it is certainly a prerequisite. Climate services aim to make climate data and information accessible for climate-sensitive decision-making. However, the grounding of climate services in the norms and institutions of climate science creates tensions that reduce the impact of climate services.

As human-wildlife conflicts escalate worldwide, concepts such as tolerance and acceptance of wildlife are becoming increasingly important. Yet, contemporary conservation studies indicate a limited understanding of positive human-wildlife interactions, leading to potentially inaccurate representations of human-animal encounters. Failure to address these limitations contributes to the design and implementation of poor wildlife and landscape management plans and the dismissal of Indigenous ecological knowledge. We examined Indigenous perspectives on human-wildlife coexistence in India by drawing ethnographic evidence from Kattunayakans, a forest-dwelling Adivasi community living in the Wayanad Wildlife Sanctuary in Kerala. Through qualitative field study that involved interviews and transect walks inside the forests, we found that Kattunayakans displayed tolerance and acceptance of wild animals characterized as forms of deep coexistence that involves three central ideas: wild animals as rational conversing beings; wild animals as gods, teachers, and equals; and wild animals as relatives with shared origins practicing dharmam. We argue that understanding these adequately will support efforts to bring Kattunayakan perspectives into the management of India's forests and contribute to the resolution of the human-wildlife conflict more broadly.Conocimiento Originario sobre la Coexistencia entre Humanos y Fauna en el Sur de la India ResumenConforme el conflicto humano-fauna escala a nivel mundial, los conceptos como la tolerancia y aceptación de la fauna son cada vez más importantes. Aun así, los estudios actuales sobre conservación muestran un conocimiento limitado de las interacciones positivas entre los humanos y la fauna, lo que lleva a representaciones potencialmente erróneas de los encuentros entre estos dos grupos. Las fallas al abordar estas limitaciones contribuyen al diseño e implementación de planes deficientes de manejo de fauna y paisajes y la desestimación del saber ecológico de los pueblos originarios. Analizamos las perspectivas de los pueblos originarios sobre la coexistencia entre las personas y la fauna en la India mediante la toma de evidencia etnográfica de los Kattunayakans, una comunidad Adivasi residente del bosque en el Santuario de Fauna Wayanad en Kerala. Realizamos un estudio cualitativo de campo con entrevistas y caminatas por transectos dentro del bosque. Con el estudio descubrimos que los Kattunayakans demostraron una tolerancia y aceptación por los animales silvestres caracterizada como maneras de coexistencia profunda que involucra tres ideas centrales: los animales silvestres son seres hablantes racionales; los animales como divinidades, maestros e iguales; y los animales silvestres como familiares practicantes del dharmam con orígenes compartidos. Argumentamos que el entendimiento de estas ideas centrales respaldará los esfuerzos por incorporar las perspectivas de los Kattunayakan a la gestión forestal de la India y contribuirá a grandes rasgos a la solución del conflicto humano-fauna.随着世界范围内人类与野生动物的冲突不断升级, 对野生动物的宽容和接纳等概念变得越来越重要。然而, 当代保护研究表明, 人们对人类与野生动物积极互作的认识有限, 导致可能难以准确描述人类与动物的相遇。如果不解决这些局限性, 就会导致野生动物和景观管理计划的设计和实施存在不足, 以及对本土生态知识的否定。我们利用印度喀拉拉邦瓦亚纳德野生动物保护区森林中的阿迪瓦西族Kattunayakans部落的民族志证据, 来研究印度原住民对于人类与野生动物共存的观点。通过定性的实地研究(包括访谈和在森林样带调查), 我们发现Kattunayakans部落表现出对野生动物的宽容和接纳, 其特点是深入的共存, 包括三个中心思想:野生动物是有理性的交流者;野生动物是神、老师和平等的族群;野生动物是实行 “dharmam”的具有共同起源的亲戚。我们认为, 充分理解以上几点有助于将Kattunayakan部落的观点引入印度的森林管理, 并更广泛地解决人类与野生动物的冲突。.

Researchers are making use of new gene-editing techniques in medicine, bioenergy, industrial biotechnology, and beyond, and the field of crop breeding is no exception. These techniques, which differ from genetic modification techniques, spell difficult questions for regulatory oversight: will current rules-of-play apply, or do new techniques necessitate fundamental shifts in regulations? Thus far, little explicit attention has focused on the fundamental yet elusive questions of which technical specifics currently trigger regulation of gene-edited crops, and where different jurisdictions ‘draw’ this line. Here, we trace these regulatory lines across key jurisdictions. We argue that extant regulatory definitions are crumbling in the face of emerging technologies and assert that this breakdown poses a threat to responsible governance. Drawing upon insights from responsible research and innovation, we propose a shift away from technically based regulatory approaches and toward more risk-targeted oversight based on broader societal and ecological implications.

Economic growth, urbanization and changes in lifestyles are leading to a transition from traditional fuels to liquefied petroleum gas (LPG) and electricity in Indian households. We use National Sample Survey data – 43rd (1987–88) and 68th (2011−12) rounds – to show that contrary to concerns about rising greenhouse gas (GHG) emissions from fossil-fuel use, switching to LPG and electricity provides health and climate benefits, if non-Kyoto emissions are considered. Our modelled household energy transition scenarios (2011–2030) show that: one, adoption of LPG by 2030 in Business-As-Usual (BAU) projections results in 80% of urban India meeting the World Health Organization's (WHO) indoor PM 2.5 guidelines. In rural households, persistent use of firewood does not significantly improve indoor air quality across the income spectrum. Two, BAU scenario projects only a 3% rise in rural GHG emissions by 2030, driven by a transition away from kerosene lighting in spite of rising electricity consumption. Three, a complete transition to LPG and electricity by 2030 reduces PM 2.5 exposure to below WHO guidelines across all urban and rural households. This is not achieved in the other partial transition scenarios. Four, across scenarios improving coal-based power plant efficiency to 40% and increasing renewables' share to 40% lead to a decrease of 14–18% in GHG emissions by 2030 relative to 2011. Finally, improved biomass cookstoves can reduce indoor exposure by 75–86% but not below the WHO guideline, and result in higher GHG emissions compared to LPG replacement due to non-Kyoto emissions from burning firewood.

Ordinary least squares linear regression (LR) has long been a popular choice among researchers interested in using historical data for estimating crop yield response to climate change. Today, the rapidly growing field of machine learning (ML) offers a wide range of advanced statistical tools that are increasingly being used for more accurate estimates of this relationship. This study compares LR to a popular ML technique called boosted regression trees (BRTs). We find that BRTs provide a significantly better prediction accuracy compared to various LR specifications, including those fitting quadratic and piece-wise linear functions. BRTs are also able to identify break points where the relationship between climate and yield undergoes significant shifts (for example, increasing yields with precipitation followed by a plateauing of the relationship beyond a certain point). Tests we performed with synthetically simulated climate and crop yield data showed that BRTs can automatically account for not only spatial variation in climate–yield relationships, but also interactions between different variables that affect crop yields. We then used both statistical techniques to estimate the influence of historical climate change on rice, wheat, and pearl millet in India. BRTs predicted a considerably smaller negative impact compared to LR. This may be an artifact of BRTs conflating time and climate variables, signaling a potential weakness of models with excessively flexible functional forms for inferring climate impacts on agriculture. Our findings thus suggest caution while interpreting the results from single-model analyses, especially in regions with highly varied climate and agricultural practices.

Commercial agriculture is central to problems of sustainability in food, water, energy and climate change. Appropriate solutions will depend on the effective promotion, monitoring and evaluation of changes in farming practice. Conservation agriculture (CA) is an important example of sustainable intensification and climate-smart agriculture, increasing the productivity and reliability of grain production while reducing agricultural inputs and future climate risks when adopted comprehensively. But to understand its implementation and benefits, researchers often rely on simplified measures of CA adoption (e.g., single proxies, binary measures, broad self-assessments, expert estimates). Here we use a national survey of South Africa’s commercial grain farmers (n = 441), contextualized by previous interviews, to investigate common measures of adoption and their implications for CA’s promotion, monitoring and evaluation. These farmers are unusually informative, because they are unsubsidized but have the capacity, incentive and willingness to adapt to climate change. We find that they are adopting CA autonomously, but that their implementation is highly variable and their interpretation of farming practice differs from that of local experts. Single proxies, binary adoption variables and broad farmer self-assessments suggest that between 40 and 80% of farmers have adopted CA. However, when evaluated across the three CA principles using UN-defined adoption thresholds, the comprehensive adoption rate is only 14%. Farmers’ definition of “conservation” differs substantially from that of the local experts most likely to be asked to contribute adoption estimates to global monitoring efforts, creating the potential for miscommunication. There is therefore substantial cause for concern in how CA is currently promoted, monitored and evaluated. Inaccurate adoption estimates jeopardize CA’s potential as a climate change adaptation strategy, creating illusory progress that may disincentivize further substantive efforts towards agricultural sustainability and climate resilience.

Despite long-standing assertions that climate change creates new risk management challenges, the climate change adaptation literature persists in assuming, both implicitly and explicitly, that weather and climate variability are suitable proxies for climate change in evaluating farmers’ risk perceptions and predicting their adaptive responses. This assumption persists in part because there is surprisingly little empirical evidence either way, although case studies suggest that there may be important differences. Here, we use a national survey of South Africa’s commercial grain farmers (n = 389)—similar to their peers in higher-income countries (e.g., North America, Europe, Australia), but without subsidies—to show that they treat weather and climate change risks quite differently. We find that their perceptions of climate change risks are distinct from and, in many regards, oppositional to their perceptions of weather risks. While there seems to be a temporal element to this distinction (i.e., differing concern for short-term vs long-term risks), there are other differences that are better understood in terms of normalcy (i.e., normal vs abnormal relative to historical climate) and permanency (i.e., temporary vs permanent changes). We also find an interaction effect of education and political identity on concern for climate change that is at odds with the well-publicized cultural cognition thesis based on surveys of the American public. Overall, studies that use weather and climate variability as unqualified proxies for climate change are likely to mislead researchers and policymakers about how farmers perceive, interpret, and respond to climate change stimuli.

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent-based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large-scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision-making techniques. These results may inform continued research on such behavioral tendencies in narrower lab- and modeling-based studies.

In an incredible story of human adaptation, the aggregate global risk of mortality to extreme weather declined by over two orders of magnitude over the past century. Yet the data show that large losses of lives to extreme weather disasters persist in nations typified by poor economic development, weak institutions, and political instability. And currently we are seeing spikes in mortality from extreme heat events in rich nations, including a wave of new reported deaths in Japan, Europe, and Canada during 2018. These events and future projections of increasing exposure suggest that we need to revisit adaptation strategies to deal with the adverse effects of extreme weather disasters across the world.

How climate-sensitive actors—like commercial farmers—perceive, understand, and react to weather and climate stimuli will ultimately determine the success or failure of climate change adaptation policies. Many studies have characterized farmers’ climate risk perceptions or farming practices, but few have evaluated the in situ decision-making processes that link (or fail to link) risk perceptions to adaptive behaviors. Here, we use a novel methodology to reveal patterns in climate-sensitive decision-making by commercial grain farmers in South Africa. We structure, linguistically code, and statistically analyze causal relationships described in 30 mental models interviews. We show that farmers’ framing of weather and climate risks strongly predicts their adoption of conservation agriculture (CA)—climate-resilient best practices that reduce shorter-term financial and weather risks and longer-term agronomic risks. These farmers describe weather and climate risks using six exhaustive and mutually exclusive languages: agricultural, cognitive, economic, emotional, political, and survival. The prevalence of agricultural and economic language only weakly predicts CA practice, whereas emotional and farm survival language strongly limits CA adoption. The framing of weather risks in terms of farm survival impedes adaptations that are likely to improve such survival in the longer term. But this survival framing is not necessarily indicative of farmers’ current economic circumstances. It represents a consequential mindset rather than a financial state and it may go undetected in more conventional studies relying on direct survey or interview questions.

Experts recommend that decision-makers in climate-vulnerable sectors integrate, or ‘mainstream’, climate change adaptation into their decision-making. Farmers are often thought to do so intuitively, because many climate change impacts will manifest in similar ways to the weather and climate variability that farmers have always faced. However, there is little evidence to suggest whether farmers are already doing this, how they should go about it, and how hard it might be. Here we show that commercial grain farmers in South Africa (N = 90), as a uniquely informative group, are struggling to mainstream climate change risk management despite their apparent incentive, capacity and willingness to adapt. They perform large-scale, highly mechanized, input-intensive grain farming like their peers in higher-income countries (e.g., the United States, Canada, Europe and Australia), but without the government subsidies, crop insurance and irrigation more common in other regions. They are therefore motivated to adapt proactively because they are more vulnerable to the financial harms of weather and climate risks. Our data show that they are explicitly sensitive to the risks of climate change, generally expressing concern for its potential impacts, reporting observed changes, proposing possible adaptations, and expressing the desire to adapt proactively. However, their mental models of climate change (n = 30) are linguistically and structurally isolated from their mental models of weather and other ‘normal’ risks. They are therefore implicitly insensitive to climate change, making it unlikely that they will adapt proactively and rationally to this uncertain risk that they otherwise appear well-equipped to manage.

Many Indian cities are investing in mass transit projects, such as metro rail networks and bus rapid transit (BRT) systems. In addition to mass transit, the development of an effective public transport network also requires the integration of many thousands of local paratransit operators who provide mobility in areas poorly served by the formal public transport system. In New Delhi battery rickshaws have emerged as one such mode in a dramatic way. In 2010, there were no battery rickshaws, but by 2013, there were an estimated 100,000, operated primarily by individual owner-drivers. This proliferation came despite a complete absence of state involvement: battery-rickshaws operated outside the ambit of the regulation, and in defiance of the city’s very highly regulated paratransit market. This paper aims to identify the causal factors behind their emergence, spread and eventual formalisation based on fieldwork carried out in New Delhi in 2014–2015. In doing this it makes arguments about their role and political-economy. It uses survey data to show that battery-rickshaws provide “last mile connectivity” to metro stations, a market in which they are out-competing all other modes including low-cost cycle-rickshaws. It argues that the demographic advantages enjoyed by battery-rickshaw operators enabled their constitution as a voting bloc in the Delhi Legislative election in 2014, which effectively guaranteed formalisation on concessionary terms. Regulators must consider the role of paratransit as an employer, rather than focusing on legality, as they seek to meet the rising demand for last-mile services in urban areas of the developing world.

India has over 800 million people without access to clean cooking fuel. A well-known, but under-researched aspect of poor access to clean energy is its cost on woman’s health and well being. Here we use the nationally representative India Human Development Survey, tracking the same set of households from 2005 to 2011, to quantify the gender-related health and time-saving benefits of a shift in a household’s fuel and stove use patterns. We show that across India, the predicted probabilities of cough in non-smoking women are 30%-60% higher than non-smoking men in solid-fuel using households, but that a complete transition from solid fuels to liquefied petroleum gas (LPG) for cooking reduces this gap to only 3%. Exclusive use of LPG is also accompanied by reduced cooking time (~37 min) and less time for collecting fuels (~24 min) in rural households, together saving up to an hour in demands on women’s labour each day. We also find electrification reduces the probability of developing cough by about 35–50% in non-smoking men and women across both rural and urban households, and help close the gap between men and women in rural households. Despite clean energy being a long-held policy goal of Indian governments, between 2005 and 2011, only 9% of households made a complete transition to clean energy, and 16.4% made a partial transition. We suggest that government efforts in India, and elsewhere, should focus on improving affordability, supply and reliability of clean fuels in enabling a complete household energy transition and help address key issues in gender inequality.

Auto-rickshaws play an important role in urban transport in India. Despite this role, auto-rickshaws and their drivers face considerable criticism from the public, the media and policy makers. There is a contentious public debate about the perceived faults of auto-rickshaws and their drivers, and the policies to address these issues in Indian cities. Our objective is to provide balance and nuance to this debate, and to enable the perspective of drivers to be more effectively considered, along with that of auto-rickshaw users and the wider travelling public, in policy-making. To this end, we critically discuss the criticism and underlying perceptions; highlight the niche role of auto-rickshaws in urban transport; and present an investigation of the realities and economics of auto-rickshaw ownership and operation. The actual congestion, safety and air pollution impacts of auto-rickshaws are at strong variance with the criticisms and perceptions on the part of the public, media and policy makers. The realities of auto-rickshaw operation are extremely challenging, and unlikely to place the driver and his family above the poverty line, which may drive some of the actions, such as not going by the meter. Finally, we critically assess policy recommendations to address the issues related to auto-rickshaws and their drivers, and offer our own suggestions regarding open permit systems, improved access to formal sector credit, a timetable for regular fare revision and the phasing out of auto-rickshaws with two-stroke engines.

This paper attempts to provide a starting point for discussion on how smartphone-based taxi applications (‘apps’) have changed the market for taxi journeys and the resulting implications for taxi market regulation. The paper focuses on the taxi apps and their impact on taxi markets. It provides a brief history of taxi regulation before outlining the underlying economic rationales of its current form in many parts of the world, characterised as the “QQE” framework (quantity, quality and economic controls on operators). It argues that current regulation assumes that taxi markets are subject to three sets of problems that require correction by regulatory intervention, namely: those associated with credence goods, problems related to open access and those resulting from transactions occurring in a thin market. It is then proposed that taxi apps solve both the credence good and thin market problems whilst largely mitigating the problems associated with open access. The paper then presents some potential problems for taxi apps, namely the potential for instability on supply and demand sides, collusion and monopoly. It also discusses concerns about driver background checks and safety. The paper concludes by arguing that instead of restricting the growth of the taxi market, regulators should focus on reducing the likelihood of monopoly and collusion in a taxi market led by apps.

This paper presents a Geographical Information System (GIS) based probabilistic simulation framework to estimate PM2.5 population exposure in New Delhi, India. The framework integrates PM2.5 output from spatiotemporal LUR models and trip distribution data using a Gravity model based on zonal data for population, employment and enrollment in educational institutions. Time-activity patterns were derived from a survey of randomly sampled individuals (n = 1012) and in-vehicle exposure was estimated using microenvironmental monitoring data based on field measurements. We simulated population exposure for three different scenarios to capture stay-at-home populations (Scenario 1), working population exposed to near-road concentrations during commutes (Scenario 2), and the working population exposed to on-road concentrations during commutes (Scenario 3). Simulated annual average levels of PM2.5 exposure across the entire city were very high, and particularly severe in the winter months: ∼200 μg m(-3) in November, roughly four times higher compared to the lower levels in the monsoon season. Mean annual exposures ranged from 109 μg m(-3) (IQR: 97-120 μg m(-3)) for Scenario 1, to 121 μg m(-3) (IQR: 110-131 μg m(-3)), and 125 μg m(-3) (IQR: 114-136 μ gm(-3)) for Scenarios 2 and 3 respectively. Ignoring the effects of mobility causes the average annual PM2.5 population exposure to be underestimated by only 11%.

Groundwater irrigation using electric pumps plays a key role in India’s agricultural water supply. Power utilities across different states use two common tariff modes to charge groundwater consumers: flat tariffs, where payments are fixed according to a pump’s power rating, and metered tariffs based on units of power actually consumed. In this review, we use empirical evidence from past studies across multiple jurisdictions in India to compare the two tariff structures in terms of three key features: administrative burden on utilities; equity of groundwater access between high-income and low-income farmers; and influence on farmers’ pumping behavior. Our analysis shows that flat tariffs have low administrative costs and more equitable distributional outcomes, but provide no incentive to farmers for water conservation. Conversely, metered tariffs have the potential to encourage judicious consumption, but are expensive to manage and disadvantageous to low-income farmers who often buy water from wealthier groundwater well owners. Flawed tariff policies, in conjunction with large subsidies for agricultural power, have caused rapid groundwater depletion in many regions as well as massive financial losses to power utilities and governments – both state and central. Since there is considerable heterogeneity in agricultural practices and groundwater availability across India, we propose location-specific strategies for rationalizing agricultural power tariffs in different regions. While the groundwater-abundant eastern regions can benefit from a hybrid flat-cum-metered tariff that encourages farmer-to-farmer water sales, western states facing unsustainable groundwater exploitation should develop tariff policies that ration power, prioritize its supply during the most critical seasons, and reward farmers who reduce their groundwater consumption. Not only will such tariff policies help conserve groundwater, but also augment government financial resources for social welfare programs such as education, health, energy access etc. Thus, improved power policies can provide substantial assistance in India’s progress towards multiple UN Sustainable Development Goals.

The responsible development of new nanomaterials and nano-enabled products requires that potential risks are understood and managed before harms occur. Although quantitative and predictive tools for anticipating human health and environmental risk are in early stages of development, there is a clear need for screening methodologies to inform decision making related to nanomaterial risk management in regulatory agencies and industry. This paper presents the results of a two-day workshop with nanotechnology experts aimed at developing a risk-screening framework for nanomaterials. Drawing upon expertise in nanotoxicology, human exposure, environmental fate and transport, and structured decision making, participants developed a decision support framework relating key nanomaterial physicochemical and product characteristics to important hazard and exposure indicators. Application of the preliminary nano-risk-screening tool (NRST) to several test cases illustrates the utility of the approach for both identifying nanomaterial characteristics that drive risks and for highlighting opportunities to redesign products to minimize risks. This framework for aiding risk managers’ decisions under uncertainty provides the foundation for the development of a transparent and adaptable screening tool that can inform the management of potential risks.

Coastal environments are some of the most populated on Earth, with greater pressures projected in the future. Managing coastal systems requires the consideration of multiple uses, which both benefit from and threaten multiple ecosystem services. Thus understanding the cumulative impacts of human activities on coastal ecosystem services would seem fundamental to management, yet there is no widely accepted approach for assessing these. This study trials an approach for understanding the cumulative impacts of anthropogenic change, focusing on Tasman and Golden Bays, New Zealand. Using an expert elicitation procedure, we collected information on three aspects of cumulative impacts: the importance and magnitude of impacts by various activities and stressors on ecosystem services, and the causal processes of impact on ecosystem services. We assessed impacts to four ecosystem service benefits — fisheries, shellfish aquaculture, marine recreation and existence value of biodiversity—addressing three main research questions: (1) how severe are cumulative impacts on ecosystem services (correspondingly, what potential is there for restoration)?; (2) are threats evenly distributed across activities and stressors, or do a few threats dominate?; (3) do prominent activities mainly operate through direct stressors, or do they often exacerbate other impacts? We found (1) that despite high uncertainty in the threat posed by individual stressors and impacts, total cumulative impact is consistently severe for all four ecosystem services. (2) A subset of drivers and stressors pose important threats across the ecosystem services explored, including climate change, commercial fishing, sedimentation and pollution. (3) Climate change and commercial fishing contribute to prominent indirect impacts across ecosystem services by exacerbating regional impacts, namely sedimentation and pollution. The prevalence and magnitude of these indirect, networked impacts highlights the need for approaches like this to understand mechanisms of impact, in order to develop strategies to manage them.

[This corrects the article DOI: 10.1371/journal.pone.0182233.].

The elicitation of expert judgment is an important tool for assessment of risks and impacts in environmental management contexts, and especially important as decision-makers face novel challenges where prior empirical research is lacking or insufficient. Evidence-driven elicitation approaches typically involve techniques to derive more accurate probability distributions under fairly specific contexts. Experts are, however, prone to overconfidence in their judgements. Group elicitations with diverse experts can reduce expert overconfidence by allowing cross-examination and reassessment of prior judgements, but groups are also prone to uncritical "groupthink" errors. When the problem context is underspecified the probability that experts commit groupthink errors may increase. This study addresses how structured workshops affect expert variability among and certainty within responses in a New Zealand case study. We find that experts' risk estimates before and after a workshop differ, and that group elicitations provided greater consistency of estimates, yet also greater uncertainty among experts, when addressing prominent impacts to four different ecosystem services in coastal New Zealand. After group workshops, experts provided more consistent ranking of risks and more consistent best estimates of impact through increased clarity in terminology and dampening of extreme positions, yet probability distributions for impacts widened. The results from this case study suggest that group elicitations have favorable consequences for the quality and uncertainty of risk judgments within and across experts, making group elicitation techniques invaluable tools in contexts of limited data.

Air pollution in New Delhi, India, is a significant environmental and health concern. To assess determinants of variability in air pollutant concentrations, we develop land use regression (LUR) models for fine particulate matter (PM2.5), black carbon (BC), and ultrafine particle number concentrations (UFPN). We used 136 h (39 sites), 112 h (26 sites), 147 h (39 sites) of PM2.5, BC, and UFPN data respectively, to develop separate morning (0800-1200) and afternoon (1200-1800) models. Continuous measurements of PM2.5 and BC were also made at a single fixed rooftop site located in a high-income residential neighborhood. No continuous measurements of UFPN were available. In addition to spatial variables, measurements from the fixed continuous monitoring site were used as independent variables in the PM2.5 and BC models. The median concentrations (and interquartile range) of PM2.5, BC, and UFPN at LUR sites were 133 (96-232) μg m(-3), 11 (6-21) μg m(-3), and 40 (27-72) × 10(3) cm(-3) respectively. In addition (a) for PM2.5 and BC, the temporal variability was higher than the spatial variability; (b) the magnitude and spatial variability in pollutant concentrations was higher during morning than during afternoon hours. Further, model R(2) values were higher for morning (for PM2.5, BC, and UFPN, respectively: 0.85, 0.86, and 0.28) than for afternoon models (0.73, 0.69, and 0.23); (c) the PM2.5 and BC concentrations measured at LUR sites all over the city were strongly correlated with measured concentrations at a fixed rooftop site; (d) spatial patterns were similar for PM2.5 and BC but different for UFPN; (e) population density and road variables were statistically significant predictors of pollutant concentrations; and (f) available geographic predictors explained a much lower proportion of variability in measured PM2.5, BC, and UFPN than observed in other LUR studies, indicating the importance of temporal variability and suggesting the existence of uncharacterized sources.

There are many advantages of solar photovoltaic technology in providing lighting for rural homes—scalability, minimal maintenance and well developed business models. This study seeks to study the impact of the transition from Compact Fluorescent Lamp (CFL) to Light Emitting Diodes based lights (LED) on the solar home lighting system market. Changing the lighting fixtures from CFL to LED would lead to the requirement of smaller panels and batteries and consequently, a reduction in prices. Would this reduction significantly increase the adoption of these systems? Would the requirement for financing or government support change? To understand this changing landscape of the rural solar lighting industry, this study analyses the current products, distribution network, and operations of seven diverse solar firms operating in different parts of India. Four of these firms exclusively make LED based products – lanterns and small home lighting systems – and the rest have some LED based systems in their portfolio. There are several factors to be considered, product configuration, luminosity, price effects and service and maintenance. While the price reduction is found to be significant (about 20%), affordability may still remain an issue for poorer households. LED lighting allows for the introduction of plug and play systems, and reduces institutional requirements for installation and maintenance.

Engineered nanomaterials (ENMs) promise great benefits for society, yet our knowledge of potential risks and best practices for regulation are still in their infancy. Toward the end of better practices, this paper analyzes U.S. federal environmental, health, and safety (EHS) regulations using a life cycle framework. It evaluates their adequacy as applied to ENMs to identify gaps through which emerging nanomaterials may escape regulation from initial production to end-of-life. High scientific uncertainty, a lack of EHS and product data, inappropriately designed exemptions and thresholds, and limited agency resources are a challenge to both the applicability and adequacy of current regulations. The result is that some forms of engineered nanomaterials may escape federal oversight and rigorous risk review at one or more stages along their life cycle, with the largest gaps occurring at the postmarket stages, and at points of ENM release to the environment. Oversight can be improved through pending regulatory reforms, increased research and development for the monitoring, control, and analysis of environmental and end-of-life releases, introduction of periodic re-evaluation of ENM risks, and fostering a "bottom-up" stewardship approach to the responsible management of risks from engineered nanomaterials.

Information and communications technology (ICT) contributes substantially to global greenhouse gas (GHG) pollutant emissions, but it is time-consuming to estimate the environmental impacts caused by the production of ICT devices, and the literature lacks coverage for newer products. Using a process-sum life cycle assessment (LCA) approach, we estimate and compare the embodied GHG emissions of 11 ICT products, including large- and small-form-factor desktop and laptop personal computers, a thin client device, an LCD monitor, newer mobile devices (an Apple iPad, an iPod Touch, and an Amazon Kindle), a rack server, and a network switch. Full bills of materials are provided via hand disassembly and weighing and are mapped to processes in the ecoinvent v2.2 database to produce impact estimates. Results are analyzed to develop simplified impact estimation models using linear regressions based on product characteristics. A simple and robust linear relationship between mass and embodied emissions is identified; a more sophisticated linear model using display mass, battery mass, and circuit board mass as inputs is slightly more accurate. Embodied GHG emissions for newer products are 50-60% lower than corresponding older products with similar functionality, largely due to decreased material usage, especially reductions in integrated circuit content.

Emerging technologies yield many benefits for society; yet new technologies are inherently accompanied by significant uncertainty surrounding potential human and environmental health risks. Such uncertainty can make it difficult for risk managers to understand unintentional impacts and to mitigate impacts through regulation, prescriptions for safe use, or the redesign of products. In circumstances of high uncertainty, expert judgment can play a central role in risk analysis. This chapter explores the limitations of traditional risk assessment tools under high uncertainty, and provides guidance on utilizing expert judgment to better understand potential risks from emerging technologies. The case of emerging nanotechnologies is investigated in detail to demonstrate the value and challenges of utilizing expert judgment in characterizing nanoparticle behavior and potential health risks.

Chassis dynamometer tests were conducted on 40 Indian auto-rickshaws with 3 different fuel–engine combinations operating on the Indian Drive Cycle (IDC). Second-by-second (1 Hz) data were collected and used to develop velocity-acceleration look-up table models for fuel consumption and emissions of CO 2 , CO, total hydrocarbons (THC), oxides of nitrogen (NO x ) and fine particulate matter (PM2.5) for each fuel–engine combination. Models were constructed based on group-average vehicle activity and emissions data in order to represent the performance of a ‘typical’ vehicle. The models accurately estimated full-cycle emissions for most species, though pollutants with more variable emission rates (e.g., PM 2.5 ) were associated with larger errors. Vehicle emissions data showed large variability for single vehicles (‘intra-vehicle variability’) and within the test group (‘inter-vehicle variability’), complicating the development of a single model to represent a vehicle population. To evaluate the impact of this variability, sensitivity analyses were conducted using vehicle activity data other than the IDC as model input. Inter-vehicle variability dominated the uncertainty in vehicle emission modeling. ‘Leave-one-out’ analyses indicated that the model outputs were relatively insensitive to the specific sample of vehicles and that the vehicle samples were likely a reasonable representation of the Delhi fleet. Intra-vehicle variability in emissions was also substantial, though had a relatively minor impact on model performance. The models were used to assess whether the IDC, used for emission factor development in India, accurately represents emissions from on-road driving. Modeling based on Global Positioning System (GPS) activity data from real-world auto-rickshaws suggests that, relative to on-road vehicles in Delhi, the IDC systematically under-estimates fuel use and emissions; real-word auto-rickshaws consume 15% more fuel and emit 49% more THC and 16% more PM 2.5 . The models developed in this study can be used to further explore the impact of varying vehicle activity patterns on emissions in efforts to manage air quality and mitigate air pollution exposure and air pollution related health impacts.

Summary Life cycle assessment (LCA) studies of desktop personal computers (PCs) are analyzed to assess the environmental impact of PCs and to explain inconsistencies and disagreements across existing studies. Impacts, characterized in this work in terms of primary energy demand and global warming potential, are decomposed into inventory components and impact per component in order to expose such inconsistencies. Additional information from related studies, especially regarding use-phase energy consumption, helps interpret the LCA results. The weight of evidence strongly suggests that for primary energy demand and contribution to climate change, the use phase is the dominant life cycle phase; manufacturing impacts are smaller but substantial, and impacts due to product transportation and end-of-life activities are much smaller. Each of the few LCA studies that report manufacturing impacts as being greater than use-phase impacts make unrealistically low assumptions regarding use-phase energy consumption. Estimates of manufacturing impacts, especially those related to printed circuit boards and integrated circuits, are highly uncertain and variable; such estimates are very difficult to evaluate, and more systematic research is needed to reduce these uncertainties. The type of computer analyzed, such as low-power light desktop or high-power workstation, may dominate the total impact; future studies should therefore base their estimates on a large sample to smooth out this variation, or explicitly restrict the analysis to a specific type of computer.

Particles were collected from the exhaust of Indian autorickshaws with natural gas and gasoline-fueled spark-ignited engines. Transmission electron microscopy was used to determine the size and shape of 2121 systematically selected particles. Particles were largely soot agglomerates and other types documented in the literature, but approximately 10% of the nonvolatile particles were multiwalled carbon nanotubes and fullerenes, forms of crystalline carbon distinct from soot. Autorickshaw fullerenic particle number emissions can be above 1011 per kg of fuel consumed. The nanotubes identified from the exhaust of autorickshaws average 168 nm in length. This is shorter than those nanotubes of greatest health concern, but given the paucity of toxicological data on carbon nanotubes and fullerenes, the potential environmental abundance from engine sources warrants closer attention. In particular, a broader range of engine types should be considered. [Supplemental materials are available for this article. Please g...

The health and climate impacts of available household cooking options in developing countries vary sharply. Here, we analyze and compare these impacts (health; climate) and the potential co-benefits from the use of fuel and stove combinations. Our results indicate that health and climate impacts span 2 orders of magnitude among the technologies considered. Indoor air pollution is heavily impacted by combustion performance and ventilation; climate impacts are influenced by combustion performance and fuel properties including biomass renewability. Emission components not included in current carbon trading schemes, such as black carbon particles and carbon monoxide, can contribute a large proportion of the total climate impact. Multiple ‘improved’ stove options analyzed in this paper yield roughly equivalent climate benefits but have different impacts on indoor air pollution. Improvements to biomass stoves can improve indoor air quality, which nonetheless remains significantly higher than for stoves that use liquid or gaseous hydrocarbons. LPG- and kerosene-fueled stoves have unrivaled air quality benefits and their climate impacts are also lower than all but the cleanest stoves using renewable biomass. & 2011 Elsevier Ltd. All rights reserved.

This paper presents a quantitative analysis of international representation in the activities of the Intergovernmental Panel on Climate Change using expert authorship counts by country in each of the four IPCC assessment reports (1990, 1995, 2001 and 2007). Overall, we find that 45% of countries, all Non-Annex 1, have never had authors participate in the IPCC process; on the other hand, European and North American experts are make up more than 75% of all authors ( N = 4394). Generalized linear models using negative binomial regression were used to quantitatively estimate the effect of a number of socio-economic, environmental and procedural factors influencing country-level participation in the IPCC. Per capita gross domestic product, population, English-speaking status, and levels of tertiary education were all found to be statistically significant drivers of authorship counts. In particular, participation by authors from English-speaking Non-Annex 1 countries is 2.5 times greater than those that are non-English speaking. Regionally small island nations of Oceania were the most severely under-represented group. South American and Asian countries had fewer authors, and African countries had more authors than what might be expected on the basis of demographic and socio-economic data. These differences across nations partly reflect existing scientific capacity that will be slow to change. However, the on-going under-representation of developing country scientists in the IPCC, particularly in the assessment of climate science (WGI) and climate mitigation (WGIII) warrants greater efforts to close the capacity gap.

Governments around the world see biofuels as a common solution to the multiple policy challenges posed by energy insecurity, climate change and falling farmer incomes. The Indian government has enthusiastically adopted a second-generation feedstock - the oilseed-bearing shrub, Jatropha curcas - for an ambitious national biodiesel program. Studies estimating the production capacity and potential land use implications of this program have typically assumed that the 'waste land' slated for Jatropha production has no economic value and that no activities of note will be displaced by plantation development. Here we examine the specific local impacts of rapid Jatropha plantation development on rural livelihoods and land use in Rajasthan, India. We find that in Jhadol Tehsil, Jatropha is planted on both government and private land, and has typically displaced grazing and forage collection. For those at the socioeconomic margins, these unconsidered impacts counteract the very benefits that the biofuel programs aim to create. The Rajasthan case demonstrates that local land-use impacts need to be integrated into decision-making for national targets and global biofuel promotion efforts. (C) 2011 Elsevier Ltd. All rights reserved.

Auto-rickshaws are important for-hire transport in many Asian cities. A survey conducted in Delhi, India of natural gas-fueled auto-rickshaws to determine activity factors, fuel consumption and air emissions used an observational inspection for oil residue in the tailpipe and visible smoke at engine start-up to classify vehicles as low- or high-PM emitters; this method was calibrated using chassis-dynamometer PM measurements. Delhi auto-rickshaws were found to travel approximately 150 km daily. Auto-rickshaws with 2-stroke engines had about 20% higher fuel consumption and CO2 emissions, and a much higher likelihood of being categorized as high-PM emitters, than those with 4-stroke engines. Within the group of 4-stroke vehicles, age was a highly significant predictor with older model years having a higher likelihood of being high-PM emitters. The results suggest that the observational procedure for visible smoke and oil could be used to rapidly identify potential PM “super-emitters” for further testing.

Auto-rickshaws in India use different fuels and engine technologies, with varying emissions and implications for air quality and climate change. Chassis dynamometer emission testing was conducted on 30 in-use auto-rickshaws to quantify the impact of switching from gasoline to compressed natural gas (CNG) in spark-ignition engines. Thirteen test vehicles had two-stroke CNG engines (CNG-2S) and 17 had four-stroke CNG engines (CNG-4S), of which 11 were dual-fuel and operable on a back-up gasoline (petrol) system (PET-4S). Fuel-based emission factors were determined for gaseous pollutants (CO(2), CH(4), NO(X), THC, and CO) and fine particulate matter (PM(2.5)). Intervehicle variability was high, and for most pollutants there was no significant difference (95% confidence level) between "old" (1998-2001) and "new" (2007-2009) age-groups within a given fuel-technology class. Mean fuel-based PM(2.5) emission factor (mean (95% confidence interval)) for CNG-2S (14.2 g kg(-1) (6.2-26.7)) was almost 30 times higher than for CNG-4S (0.5 g kg(-1) (0.3-0.9)) and 12 times higher than for PET-4S (1.2 g kg(-1) (0.8-1.7)). Global warming commitment associated with emissions from CNG-2S was more than twice that from CNG-4S or PET-4S, due mostly to CH(4) emissions. Comprehensive measurements and data should drive policy interventions rather than assumptions about the impacts of clean fuels.

This article addresses the role of scientific knowledge in decision-making with respect to climate variability and change in the developing world, with a focus on scientific capacity. We propose a ‘systemic’ view of scientific capacity for studying the relationship between science and decision-making vis-a-vis climate variation, one that encompasses knowledge production, as well as its translation for and use in decision-making. We analyze the challenges faced by developing countries in building capacity on each of these elements. Case studies on the production and use of scientific information for societal decision-making at three distinct timescales—the weekly scale (Hurricanes in the North Indian Ocean), the seasonal scale (Climate Variability in the Sahel), and the decadal/century scale (Climate Change Impacts on Small Island States) are used to elucidate the scale and complexity of capacity building challenges. We argue that capacity building for coping with the impacts of climate change is interwoven with the capacity needed for meeting the challenges of development, particularly those related to short-term climate and weather variation. Any serious attempt to build scientific capacity for decision-making vis-a-vis climate change will need to embrace a ‘developmentalist’ position. WIREs Clim Change 2011 2 201–219 DOI: 10.1002/wcc.98 For further resources related to this article, please visit the WIREs website

Despite the widespread commercial use of nanomaterials, regulators currently have a limited ability to characterize and manage risks. There is a paucity of data available on the current production and use of nanomaterials and extreme scientific uncertainty on most aspects of the risk assessment “causal chain.” Regulatory decisions will need to be made in the near-term in the absence formal quantitative risk assessments. The article draws on examples from three different regulatory contexts—baseline data monitoring efforts of the U.S. Environmental Protection Agency and California Department of Toxic Substances Control, prioritization of risk information in the context of environmental releases, and mitigation of occupational risks—to argue for the use of decision-analytic tools in lieu of formal risk assessment to help regulatory bodies. We advocate a “horses for courses” approach whereby existing analytical tools (such as risk ranking, multi-criteria decision analysis, and “control banding” approaches) might be adapted to regulators’ goals in particular decision contexts. While efforts to build new and modify existing tools are underway, they need greater support from funding and regulatory agencies because innovative approaches are needed for the “extreme” uncertainty problems that nanomaterials pose.

This article uses comparative analyses of both US and EU regulations to understand the evolution of Indian policies aimed at regulating genetically engineered agriculture (primarily cotton). India initially borrowed extensively from OECD regulations, predictably emphasizing human health and environmental impacts. Over time, however, India's biosafety regulations have evolved to reflect the twin imperatives of development and democracy. India expanded biosafety initially to address the economic security of farmers and later, and to a lesser extent, to increase citizen participation and consultation. Much of this reconstitution has been realized through public interest litigation critical of agri-biotech regulation brought about by urban civil society groups, a legal tool that may no longer be applied under India's proposed National Biotechnology Regulatory Authority. Copyright , Beech Tree Publishing.

Between 2001 and 2003, public transport vehicles in New Delhi were required to switch their fuel to natural gas in an attemptto reduce their air pollution impacts. This study examines the climatic impacts of New Delhi's fuel switching policy, and outlines implications for such efforts in rapidly industrializing countries. Natural gas is mostly composed of methane, an important greenhouse gas. Emitted aerosols (black carbon, particulate organic carbon, and sulfate) also cause radiative forcing. We find that methane and black carbon emissions are critical contributors to the change in carbon dioxide equivalent [CO2(e)] emissions. In New Delhi, the switch to natural gas results in a 30% increase in CO2(e) when the impact of aerosols is not considered. However, when aerosol emissions are taken into account in our model, the net effect of the switch is estimated to be a 10% reduction in CO2(e), and there may be as much as a 30% reduction in CO2(e). There is significant potential for emissions reductions through the United Nations Framework Convention on Climate Change (UNFCCC) Clean Development Mechanism for such fuel switching projects.

Communication of uncertainty information in Intergovernmental Panel on Climate Change (IPCC) assessments has evolved through successive reports to provide increasingly formal classifications for subjective and objective information. The first IPCC assessments provided uncertainty information in largely subjective form via linguistic categorizations depicting different levels of confidence. Recent assessments have codified linguistic terms to avoid ambiguity and introduced probabilistic ranges to express likelihoods of events occurring. The adoption of formal schemes to express likelihood and confidence provides more powerful means for analysts to express uncertainty. However, the combination of these two metrics to assess information may engender confusion when low confidence levels are matched with very high/low likelihoods that have implicit high confidence. Part of the difficulty is that the degree to which different quantities in the assessments are known varies tremendously. One solution is to provide likelihood information in a scheme with a range of different precision levels that can be matched to the level of understanding. A version of this scheme is also part of the IPCC uncertainty guidance and is described here.

This paper uses spectral methods to analyze changes in air quality at a single monitoring site in Delhi since 2000. Power spectral density calculations of daily concentration data for particulate matter (PM10), carbon monoxide (CO), oxides of nitrogen (NO x ) and oxides of sulfur (SO x ) reveal the presence of trends and periodic oscillations for all the pollutants. Singular Spectrum Analysis (SSA) is used to decompose daily data into statistically significant non-linear trends, seasonal cycles and other oscillations. Periods of sharp reductions were observed for both SO x and CO concentrations in 2001 and 2002, respectively. NO x concentration trends show a sustained rise from 2000 to 2004, followed by small decline thereafter. PM10 concentration trends remain essentially unchanged over the time period. All pollutants also show strong annual and biannual cycles. The observed trends in CO and NO x likely relate changes in Delhi's vehicular traffic emissions. The sharp drop in both the trend and amplitude of the seasonal cycle of CO coincides with the switch to Compressed Natural Gas (CNG) as a fuel for Delhi's public transport fleet. Observed changes in SO x and PM10 concentrations were most likely caused by sources unrelated to vehicular traffic.

The end-of-life vehicle (ELV) Directive in Europe aims to generate environmental gains through increased levels of vehicle recovery and a reduction in the use of hazardous substances. This paper presents an evaluation framework based on five anticipated changes that could result from the ELV Directive. These changes relate to three areas: (a) vehicle design, (b) level of ELV recovery, and (c) information provision. We evaluate the extent to which expected outcomes have materialized since the establishment of the ELV Directive. Current information provides an emerging picture of the impact of ELV legislation. We show that legislative factors and market forces have led to innovation in recycling, increased hazardous substance removal and improved information dissemination. Such actions may be sufficient to reach ELV Directive targets and could have spill-over benefits to other industries. Carmakers are also taking steps to design for recycling and for disassembly. However, movement toward design for re-use and remanufacturing seems limited. Increasing the level of re-use and remanufacturing will be a key part of moving toward sustainable vehicle production.

Uncertainties in conventional quantitative risk assessment typically relate to values of parameters in risk models. For many environmental contaminants, there is a lack of sufficient information about multiple components of the risk assessment framework. In such cases, the use of default assumptions and extrapolations to fill in the data gaps is a common practice. Nanoparticle risks, however, pose a new form of risk assessment challenge. Besides a lack of data, there is deep scientific uncertainty regarding every aspect of the risk assessment framework: (a) particle characteristics that may affect toxicity; (b) their fate and transport through the environment; (c) the routes of exposure and the metrics by which exposure ought to be measured; (d) the mechanisms of translocation to different parts of the body; and (e) the mechanisms of toxicity and disease. In each of these areas, there are multiple and competing models and hypotheses. These are not merely parametric uncertainties but uncertainties about the choice of the causal mechanisms themselves and the proper model variables to be used, i.e., structural uncertainties. While these uncertainties exist for PM2.5 as well, risk assessment for PM2.5 has avoided dealing with these issues because of a plethora of epidemiological studies. However, such studies don’t exist for the case of nanoparticles. Even if such studies are done in the future, they will be very specific to a particular type of engineered nanoparticle and not generalizable to other nanoparticles. Therefore, risk assessment for nanoparticles will have to deal with the various uncertainties that were avoided in the case of PM2.5. Consequently, uncertainties in estimating risks due to nanoparticle exposures may be characterized as ‘extreme’. This paper proposes a methodology by which risk analysts can cope with such extreme uncertainty. One way to make these problems analytically tractable is to use expert judgment approaches to study the degree of consensus and/or disagreement between experts on different parts of the exposure-response paradigm. This can be done by eliciting judgments from a wide range of experts on different parts of the risk causal chain. We also use examples to illustrate how studying expert consensus/disagreement helps in research prioritization and budget allocation exercises. The expert elicitation can be repeated over the course of several years, over which time, the state of scientific knowledge will also improve and uncertainties may possibly reduce. Results from expert the elicitation exercise can be used by risk managers or managers of funding agencies as a tool for research prioritization.

IPCC reports provide a synthesis of the state of the science in order to inform the international policy process. This task is made difficult by the presence of deep uncertainty in the climate problem that results from long time scales and complexity. This paper focuses on how deep uncertainty can be effectively communicated. We argue that existing schemes do an inadequate job of communicating deep uncertainty and propose a simple approach that distinguishes between various levels of subjective understanding in a systematic manner. We illustrate our approach with two examples. To cite this article: M. Kandlikar et al., C. R. Geoscience 337 (2005).

The problem of detection of climate change and attribution of causes of change has been formalized as a series of discrete probability judgements in an expert elicitation protocol. Here results are presented from the protocolfor 19 experts, highlighting areas of convergence and divergence among experts. There is broad agreement among the experts that the global mean surface air temperature, vertical pattern of temperature change, geographical pattern of temperature change, and changes in diurnal temperature are the important lines of evidence for climate change detectionand attribution. For the global mean and vertical pattern lines of evidence, the majority of experts (90%) reject the null hypothesis (no climate change) at the 5% significance level, thereby lending strong support to detection of climatechange. For these lines of evidence the median probability of detection at the 5% significance level across experts exceeds 0.9. For the geographical pattern and diurnal cycle lines of evidence, t...

© 2014 Beaudrie et al. Engineered nanoscale materials (ENMs) present a difficult challenge for risk assessors and regulators. Continuing uncertainty about the potential risks of ENMs means that expert opinion will play an important role in the design of policies to minimize harmful implications while supporting innovation. This research aims to shed light on the views of 'nano experts' to understand which nanomaterials or applications are regarded as more risky than others, to characterize the differences in risk perceptions between expert groups, and to evaluate the factors that drive these perceptions. Our analysis draws from a web-survey (N = 404) of three groups of US and Canadian experts: nano-scientists and engineers, nano-environmental health and safety scientists, and regulatory scientists and decision-makers. Significant differences in risk perceptions were found across expert groups; differences found to be driven by underlying attitudes and perceptions characteristic of each group. Nano-scientists and engineers at the upstream end of the nanomaterial life cycle perceived the lowest levels of risk, while those who are responsible for assessing and regulating risks at the downstream end perceived the greatest risk. Perceived novelty of nanomaterial risks, differing preferences for regulation (i.e. the use of precaution versus voluntary or market-based approaches), and perceptions of the risk of technologies in general predicted variation in experts' judgments of nanotechnology risks. Our findings underscore the importance of involving a diverse selection of experts, particularly those with expertise at different stages along the nanomaterial lifecycle, during policy development.