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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.

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.

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.

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.

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 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.

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.

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.

The potential and promise of nanotechnologies depends in large part on the ability for regulatory systems to assess and manage their benefits and risks. However, considerable uncertainty persists regarding the health and environmental implications of nanomaterials, hence the capacity for existing regulations to meet this challenge has been widely questioned. Here we draw from a survey (N=254) of US-based nano-scientists and engineers, environmental health and safety scientists, and regulatory scientists and decision-makers, to ask whether nano experts regard regulatory agencies as prepared for managing nanomaterial risks. We find that all three expert groups view regulatory agencies as unprepared. The effect is strongest for regulators themselves, and less so for scientists conducting basic, applied, or health and safety work on nanomaterials. Those who see nanotechnology risks as novel, uncertain, and difficult to assess are particularly likely to see agencies as unprepared. Trust in regulatory agencies, views of stakeholder responsibility regarding the management of risks, and socio-political values were also found to be small but significant drivers of perceived agency preparedness. These results underscore the need for new tools and methods to enable the assessment of nanomaterial risks, and to renew confidence in regulatory agencies' ability to oversee their growing use and application in society. © 2013 Beaudrie et al.

The developed world has pledged to mobilize at least US $100 billion per year of 'new' and 'additional' funds by 2020 to help the developing world respond to climate change. Tracking this finance is particularly problematic for climate change adaptation, as there is no clear definition of what separates adaptation aid from standard development aid. Here we use a historical database of overseas development assistance projects to test the effect of different accounting assumptions on the delivery of adaptation finance to the developing countries of Oceania, using machine algorithms developed from a manual pilot study. The results show that explicit adaptation finance grew to 3%–4% of all development aid to Oceania by the 2008–2012 period, but that total adaptation finance could be as high as 37% of all aid, depending on potentially politically motivated assumptions about what counts as adaptation. There was also an uneven distribution of adaptation aid between countries facing similar challenges like Kiribati, the Marshall Islands, and the Federated States of Micronesia. The analysis indicates that data allowing individual projects to be weighted by their climate change relevance is needed. A robust and mandatory metadata system for all aid projects would allow multilateral aid agencies and independent third parties to perform their own analyses using different assumptions and definitions, and serve as a key check on international climate aid promises.