Adaptive Decision Research Articles

Modeling the impact of future rainfall changes on the effectiveness of urban stormwater control measures

The convergence of urban expansion, deteriorating infrastructure, and a changing climate will escalate the risks of stormwater pollution and urban flooding in the coming decades. Using outputs from an ensemble of global climate models to drive a high spatial resolution stormwater model, we analyzed climate change impacts on urban stormwater runoff and control measures for 23 cities across the United States. Runoff model outputs for two future emissions scenarios ending in 2055 were compared against a historical scenario to assess changes. All cities showed increases in average annual stormwater runoff, with changes up to 30% over the next 30 years due to a greater frequency of high intensity storm events. Runoff model outputs showed substantial variation across cities with untreated stormwater runoff increasing by as much as 48%. Patterns of future runoff impacts within cities will affect the performance of distributed treatment strategies such as Green Stormwater Infrastructure (GSI) to meet municipal water quality improvement and runoff reduction goals. Results indicate that adoption of adaptable design standards and decision support tools that readily accommodate projected precipitation changes are critical for supporting more resilient designs of stormwater control measures.

A fine-grained robust performance diagnosis framework for run-time cloud applications

To maintain the required service quality of time-critical cloud applications, operators must continuously monitor their runtime status, detect potential performance anomalies, and diagnose the root causes of these anomalies effectively. However, existing performance diagnosis methods face challenges such as the need for high-quality labeled data, the low reusability and robustness of performance anomaly detection models, and the absence of real-time fine-grained root cause localization. These challenges make fixing performance issues quickly and developing effective adaptation decisions difficult. We provide a Fine-grained Robust Performance Diagnosis (FIRED) framework to tackle those challenges. The framework offers a metrics selection component to filter noise and improve detection efficiency, an anomaly detection component that assembles several well-selected base models with a deep neural network, and adopts weakly supervised learning considering fewer labels exist in reality. The framework also employs a real-time, fine-grained root cause localization component to locate dependent resource metrics of performance anomalies. Our experiments show that the framework can effectively reduce data noise and achieve the best accuracy and algorithm robustness for performance anomaly detection. In addition, the framework can accurately localize the first root causes, with an average accuracy higher than 0.7 for locating the first four root cause metrics.

Translating climate risk assessments into more effective adaptation decision-making: The importance of social and political aspects of place-based climate risk

Climate risk continues to be framed ostensibly in terms of physical, socio-economic and/or ecological risks, as evidenced in the 2012 and 2017 UK Climate Change Risk Assessment (CCRA) evidence reports. This article argues that framing climate risk in this way remains problematic for the science-policy process, particularly in ensuring adequate climate risk assessment information translates into more effective adaptation decision-making. We argue how climate risk assessments need to further consider the social and political aspects of place-based climate risk to ensure more effective adaptation policy outcomes. Using a discourse analysis of the CCRA3 Technical Report methods chapter published in June 2021, we discuss three critical themes around how climate risk is currently framed within the Technical Report methods chapter. These are (i) the over-reliance on reductive methodological framing of assessing climate risk through ‘urgency scores’; (ii) the idea of what constitutes ‘opportunity’; and (iii) the framing of transformational adaptation discourses through the lens of climate risk. To conclude, we suggest that to move beyond assessing risk solely in terms of biophysical and socio-economic risk, a greater emphasis on the social and political contexts of ‘place-based’ risk needs to be central to climate change risk assessments.

Rural farmers’ perceptions of and adaptations to climate change in Sub-Saharan Africa: Does climate-smart agriculture (CSA) matter in Nigeria and Ethiopia?

The available literature has ignored farmers’ perceptions on the benefits and drawbacks of adopting climate-smart agriculture (CSA) in favor of focusing primarily on profitability and economic constraints. We use the Ethiopian Socioeconomic Survey (ESS) and the General Household Survey from 2018 and 2019 to compare Nigeria and Ethiopia, both of which have sizable rural populations to assess farmers’ climate change perception and their adaptation options in promoting CSA. We first hypothesize that farmers with high tolerance for risk and stable financial resources are more likely to adopt CSA techniques, relying on the adopter perception theory of agricultural innovations and technologies. We address potential selection bias using the Heckman selection model, and estimate our data using multinomial logistic estimator, as well as standard logistic regression for robustness checks. We find that in both Ethiopia and Nigeria, household income and plot size influence farmers’ adaptations to climate change mitigation practices. However, farmers with bigger plots who run the risk of massive production loss tend to adopt measures of coping with climate change. We show that in both Ethiopia and Nigeria, rural farmers’ adaptation decisions are heavily influenced by agricultural extension programs and community social networks. Overall, our work highlights the important role of income, farm size, and climate-related information for investing in climate-smart agricultural methods to curb food insecurity in Sub-Saharan Africa.

INSTITUTIONS AND MANDATES FOR CLIMATE CHANGE ADAPTATION IN BULGARIAN RURAL AREAS

Climate change is undeniably the leading challenge in the 21st century (IPCC 2022). Agriculture is simultaneously a major driver of climate change and is seriously affected by it (EEA, 2019). The adaptation efforts and the level of readiness to adapt to climate change vary between regions, countries and sectors (e.g. agriculture and environment). The overall goal of the research is to assess the level of readiness for climate change adaptation of the agricultural and environmental institutions in Bulgaria. The specific objective of the paper is to analyse their institutional setups and mandates for adapting to climate change in the Bulgarian rural areas. The research findings and results will support the identification of enabling conditions and key barriers to stronger institutional adaptation capacities of the main agricultural and environmental institutions in the country. The assessment is based on documentary analysis of the national climate adaptation strategy and the legal acts, regulating the institutions’ mandates and functions. The methodological approach is motivated by the conceptual model developed by Ford and King (2015) for assessing the climate adaptation readiness by governments at various scales. The focus is on three of their adaptation readiness factors – political leadership on adaptation, institutional organisation for adaptation and adaptation decision making. The results indicate a reactive rather than proactive political leadership on climate adaptation in Bulgaria. The institutional organisation for adaptation suffers serious understaffing in the Ministry of Environment and Water and in the relevance sectoral ministries. The Strategy on Climate Change and Adaptation stated the need for increased capacities and training in sectoral institutions and stakeholders in 2019. In 2023, there is only one environmental institution with an official mandate on climate change – the Climate Policy Department in the Ministry of Environment and Water. Its mandate is on policy development at the global, European and national level addressing both mitigation and adaptation needs; and it is the smallest unit in the ministry in terms of staff numbers. The other environmental institution with a mandate on climate change is the Executive Environmental Agency which monitors the greenhouse gas emissions and the related permits and registers, with no explicit mandate on climate adaptation. As regards the agriculture and rural development institutions, the Rural Development Directorate in the Ministry of Agriculture has a mandate to propose measures addressing climate change needs during the programming of the Common Agricultural Policy Strategic Plan 2023-2027. The identified needs (i.e. introduce climate adapted breeds and plant species) relate to the farming sector and not to the rural areas. Overall, the key weakness of the Bulgarian climate adaptation approach in rural areas is its reactive and ad-hoc basis. It either is driven by EU regulations and requirements or is implemented when project opportunities arise, without systemic planning and ownership of responsibility in the agriculture or environmental institutions. Key words: climate change governance, adaptation readiness, agriculture JEL code: Q54, Q58, Q18

Conceptualizing Supply Chain Resilience in Exogenous Crisis Times: Toward a Holistic Definition

AbstractSupply chain resilience has become increasingly crucial in today’s scenario in response to major disruptions that generated huge exogenous crises. This paper investigates the current SCR-related academic literature related to SCR to develop a new holistic definition. A total amount of 214 scientific contributions the last 5 years (i.e., between 2018 and 2022) have been analyzed. Findings revealed four important issues to achieve SCR, i.e., Key resilience attributes, Technology-aided visible SC, Sustainable and measurable SCR practices, and Relationship between SC partners. Leveraging insights gleaned from these critical issues, we have meticulously crafted an updated and comprehensive definition of SCR, by considering the multi-faceted facets that contribute to its holistic understanding. In light of our emerging insights, we suggest a novel approach to fortify organizational supply chain resilience, emphasizing a more holistic perspective. Our recommendations for future research include (1) exploring the integration of emerging technologies like blockchain and artificial intelligence for real-time monitoring to enhance adaptive decision-making in post-COVID-19 supply chains, (2) investigating the strategic role of collaborative networks and information sharing among supply chain partners to enhance agility and responsiveness amidst post-pandemic uncertainties, and (3) analyzing the impact of sustainable and circular supply chain practices on resilience. Findings of the paper and the new holistic definition of SCR have several implications for both academics and managers that are listed at the end of the paper along with limitations of the study and future research avenues.

Dynamic load balancing in IoT-enabled WSNs using fuzzy logic-based control

The rapid expansion of Internet of Things (IoT) applications and the increasing complexity of Wireless Sensor Networks (WSNs) have created a critical need for efficient load balancing strategies. This paper proposes a dynamic load balancing approach for IoT-enabled WSNs using a fuzzy logic-based control mechanism. The proposed method aims to optimize energy consumption, reduce latency, and enhance network lifetime by intelligently distributing the workload among sensor nodes. The fuzzy logic controller takes into account various parameters, such as energy levels, communication distances, and node density, to make adaptive load balancing decisions. The control mechanism allocates tasks to the most suitable nodes, ensuring efficient utilization of resources and preventing overloading of individual nodes. Simulations are conducted in diverse network scenarios to validate the performance of the proposed approach. Results demonstrate significant improvements in energy efficiency, latency reduction, and overall network lifetime compared to traditional load balancing techniques. The fuzzy logic-based control mechanism proves to be a promising solution for addressing the dynamic and resource-constrained nature of IoT-enabled WSNs, paving the way for more robust and resilient networks in various IoT applications.

Optimizing Investment Strategies: A Case Study on JPMorgan Chase & Co. Stock Options Using the Black-Scholes Model and What-If Analysis in Excel

This research focuses on applying the Black-Scholes Model to evaluate European options on JPMorgan Chase & Co. stocks. This model has been a critical foundation in evaluating financial instruments, especially options, since its development in 1973 by Fisher Black, Myron Scholes, and Robert Merton. The study utilizes secondary data from some sources to obtain current information regarding stock prices, strike prices, expiration time, volatility, and relevant risk-free interest rates for option valuation as of December 19, 2023. Through this approach, our aim is to gain a better understanding of how the Black-Scholes Model is used as a framework in determining option prices for these stocks. The research methodology involves What-If analysis, exploring variations in key variables such as current stock price, strike price, expiration time, stock price volatility, and risk-free interest rates to assess how these changes affect the prices of both call and put options. Additionally, the study presents graphs representing stock prices, strike prices, interest rates, time, and volatility to visually support the research findings. The analysis results reveal that the prices of both call and put options are highly responsive to changing market conditions. An increase in the current stock price tends to raise the call option price while reducing the put option price. Conversely, an increase in the strike price has the opposite effect. Moreover, variations in the risk-free interest rates influence the option prices, with rising rates increasing the call option price and decreasing the put option price. Furthermore, as the expiration time approaches or stock price volatility increases, both call and put option prices tend to rise. These findings provide a comprehensive understanding of the dynamics of JPMorgan Chase & Co.'s stock option pricing, offering a foundation for investors to make informed and adaptable investment decisions amid constantly evolving financial markets. Sensitivity to changes in key variables is an essential aspect to consider in designing effective investment strategies in the face of ever-changing financial markets.

Deep learning algorithms and their fuzzy extensions for streamflow prediction in climate change framework

Abstract The present study analyzes the capability of convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM, fuzzy CNN, fuzzy LSTM, and fuzzy CNN-LSTM to mimic streamflow for Lower Godavari Basin, India. Kling–Gupta efficiency (KGE) was used to evaluate these algorithms. Fuzzy-based deep learning algorithms have shown significant improvement over classical ones, among which fuzzy CNN-LSTM is the best. Thus, it is further considered for streamflow projections in a climate change context for four-time horizons using four shared socioeconomic pathways (SSPs). Average streamflow in 2041–2060, 2061–2080, and 2081–2090 are compared to that of 2021–2040 and it changed by +3.59, +7.90, and +12.36% for SSP126; +3.62, +8.28, and +12.96% for SSP245; +0.65, −0.01, and −0.02% for SSP370; +0.02, +0.71, and +0.06% for SSP585. In addition, two non-parametric tests, namely, Mann–Kendall and Pettitt were conducted to ascertain the trend and change point of the projected streamflow. Results indicate that fuzzy CNN-LSTM provides a more precise prediction than others. The identified variations in streamflow across different SSPs facilitate valuable insights for policymakers and relevant stakeholders. It also paves the way for adaptive decision-making.

간호대학생의 진로결정 자기효능감, 전공만족도, 사회적 지지가 대학생활 적응에 미치는 영향

This study aimed to investigate factors influencing the adaptation of nursing college students to university life. A survey was conducted with 202 second-year nursing students at a university to understand the impact of career decision self-efficacy, major satisfaction, and social support on university life adaptation. The sample size determination was done using G-power 3.1.9, and SPSS 21.0 was employed for data analysis. Differences in university life adaptation based on participant characteristics were assessed using t-tests and ANOVA. Pearson's correlation coefficient was utilized to examine the correlation between career decision self-efficacy, major satisfaction, social support, and university life adaptation. Multiple regression analysis was employed to identify factors influencing university life adaptation. The results revealed positive correlations between university life adaptation and career decision self-efficacy, major satisfaction, and social support. Significant correlations were also observed between career decision self-efficacy and major satisfaction, as well as major satisfaction and social support. The most influential factors affecting university life adaptation were identified as career decision self-efficacy and major satisfaction, with the model explaining 48.3% of the variance. The study concludes that career decision self-efficacy has the greatest impact on the adaptation of nursing college students, emphasizing the need for program development to enhance this aspect. Furthermore, given the importance of major satisfaction and social support, systematic management and intervention are deemed necessary for effective university life adaptation strategies.

Diesel engine quality abnormal patterns recognition based on feature fusion and adaptive decision fusion

The current assembly process of marine diesel engines is low in intelligence and the control chart pattern classifier with unstable performance, which makes it difficult to control and identify the quality control chart pattern. This paper proposes a new assembly quality control diagram recognition method based on an adaptive decision model to address these problems. Through characteristics and changes of the diesel engine assembly process analyses, the triangular norm is used to fuse the extracted shape features and statistical features to reduce the influence of data fluctuation and imbalance on pattern recognition. An adaptive decision fusion model of the assembly process is established by defining multiple weights with considering the complexity and uncontrollability of the diesel engine assembly process. Based on these, the fusion coefficients within the adaptive decision model are optimized by the Ant Lion Optimization algorithm (ALO) to improve the decision efficiency and classification precision. To validate the proposed model, diesel engine exhaust pressure is selected as a case for abnormal pattern recognition, and the ability of the model is discussed in terms of recognition accuracy and stability.

A Bayesian adaptive design approach for stepped-wedge cluster randomized trials.

The Bayesian group sequential design has been applied widely in clinical studies, especially in Phase II and III studies. It allows early termination based on accumulating interim data. However, to date, there lacks development in its application to stepped-wedge cluster randomized trials, which are gaining popularity in pragmatic trials conducted by clinical and health care delivery researchers. We propose a Bayesian adaptive design approach for stepped-wedge cluster randomized trials, which makes adaptive decisions based on the predictive probability of declaring the intervention effective at the end of study given interim data. The Bayesian models and the algorithms for posterior inference and trial conduct are presented. We present how to determine design parameters through extensive simulations to achieve desired operational characteristics. We further evaluate how various design factors, such as the number of steps, cluster size, random variability in cluster size, and correlation structures, impact trial properties, including power, type I error, and the probability of early stopping. An application example is presented. This study presents the incorporation of Bayesian adaptive strategies into stepped-wedge cluster randomized trials design. The proposed approach provides the flexibility to stop the trial early if substantial evidence of efficacy or futility is observed, improving the flexibility and efficiency of stepped-wedge cluster randomized trials.

Mobilising transformative community-based climate change adaptation

Under-resourced and marginalised communities often have limited government support, and therefore, must decide by themselves if and how they adapt to the uncertainties of climate change. Community-based climate change adaptation (CbA) forms a plausible approach here. In practice, however, CbA projects often fail to address the underlying causes of vulnerability like power relations and sociocultural structures that influence adaptation measures adopted and outcomes. In response, this perspective paper explores the concept of transformative community-based climate change adaption (TCbA), a reflexive approach that focuses on empowering communities by reframing the decision-making context. It discusses pathways of mobilising this shift to transformative CbA in three steps: (1) specifying the characteristics of a TCbA; (2) exploring the leverage points to mobilise it, and (3) recommending a set of actions and processes that facilitate co-decided interventions at the identified leverage points. These leverage points include "the structure of information flow", "rules of the system" and “power to change system structure or self-organise". The paper concludes by emphasising that the shift to transformative CbA begins when facilitators and members of the community undertaking a CbA project are reflexive about how the societal context of decision processes, like power relations, influence community adaptation decision-making, planning and implementation.

The core functions and forms paradigm throughout EPIS: designing and implementing an evidence-based practice with function fidelity.

There are numerous frameworks for implementing evidence-based practices (EBPs) in novel settings to achieve "fidelity." However, identifying appropriate referents for fidelity poses a challenge. The Core Functions and Forms paradigm offers a model that can inform adaptation decisions throughout all phases of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. We applied the Core Functions-Forms paradigm throughout the Exploration and Preparation phases of EPIS in the design of two EBPs targeting family protective factors among Latinos in San Diego, as well as describe plans for its use in Implementation and Sustainment. We employed a distinct approach for each intervention element to contrast adaptation decisions that prioritize adherence to either form or function fidelity. We describe our application of the functions-forms paradigm within the EPIS framework, focusing on the Preparation phase. We also provide functions-forms matrices that map out the relationship between individual intervention components (forms) and the essential processes (functions) by which components are theorized to exert their impact. This case study of how the core functions-forms framework can be mapped onto EPIS can support a conceptual shift from prioritizing form fidelity to also focusing on function fidelity. This might allow interventionists to target appropriate fidelity referents when adapting an EBP, rather than defaulting to maintaining fidelity to forms as described in the protocol. We see great promise for using this framework for guiding actions throughout all EPIS phases and informing future applications of this paradigm to foster more robust fidelity to function.

Decolonizing Climate Change Adaptations from Indigenous Perspectives: Learning Reflections from Munda Indigenous Communities, Coastal Areas in Bangladesh

This study explores the imperative need for decolonizing climate change adaptation strategies by focusing on Indigenous knowledge and perspectives. Focusing on the Munda Indigenous communities residing in the coastal areas of Bangladesh, the research offers critical insights into the intricate relationship between Indigenous wisdom and sustainable climate adaptation. By engaging with the Munda Indigenous people and their traditions, this study explores how traditional ecological knowledge and practices can inform and enhance contemporary climate adaptation efforts. Following the decolonial theoretical research framework, this research used participatory research methods and collaboration with the Munda Indigenous community. In this study, we shared our learning reflections to uncover unique approaches to climate resilience, including traditional community-based disaster risk reduction and cultural practices that foster social cohesion. These insights challenge the prevailing Western-centric climate adaptation paradigms, emphasizing recognizing and valuing Indigenous voices in climate discourse. The research underscores the significance of empowering Indigenous communities as key stakeholders in climate adaptation policy and decision-making. It calls for shifting from top-down, colonial approaches towards more inclusive, culturally sensitive strategies. The Munda Indigenous communities’ experiences offer valuable lessons that can inform broader efforts to address climate change, fostering resilience and harmonious coexistence between people and their environment. This study advocates for integrating Indigenous knowledge, practices, and worldviews into climate adaptation frameworks to create more effective, equitable, and sustainable solutions for the challenges posed by climate change.

Trends and climate response in the yield of staple crops across Northeast China

Understanding how climate change has been affecting crop yield is a prerequisite to inform adaptation decisions and to ensure global food security. The Northeast China (NEC), located in high-latitude area, is significantly affected by climate change. There is a high demand for quantitative information on impacts of climate variability on crop yields in NEC. In this study, we applied panel regression models to explore the yield-climate relationship and its spatial variations based on experimentally observed yield (Ys) and the county-scale statistical yield (Yc) of three typical crops (e.g., maize, rice and soybean) across NEC from 1981 to 2010, as well as contemporary climate data. The results indicated that Ys (Yc) of maize, rice and soybean during the past three decades increased by 64.2% (82.0%), 47.5% (55.3%), and 59.2% (58.0%), respectively. Moreover, we found climate during crop growth period had changed significantly and the change had caused measurable impacts on crop yields. Changes in temperature, precipitation and solar radiation jointly decreased Ys (Yc) of maize and soybean by 1.3% (0.7%) and 0.3% (1.1%), respectively, however increased Ys (Yc) of rice by 1.2% (2.2%), with a large spatial difference. Therefore, the impact of climate change on yields of three crops was far less than the actual increase in crop yields. Our finding suggest that other factors, mainly improved management practices, may be the main reason for the significant increase in crop yields in NEC over the past few decades. Although historical climate change also played an important role in the yields of three crops, certain improvement in management measures not only compensated for the negative impact of climate change, but also greatly promoted the increase in crop yields. Therefore, in proposing strategies for crop production to cope with climate change, we need in-depth assessments of the role of improved management.

Bittersweet memories and somatic marker hypothesis: adaptive control in emotional recall facilitates long-term decision-making in the Iowa Gambling Task.

The somatic marker hypothesis states that emotional recall and its somatic influence guide long-term decision-making. However, the mechanism through which decision-making benefits from emotional recall is unclear; whether emotional recall and the induced affect increase the regulatory demand or amplify the affect state that requires inhibition. It is unclear if controlling the automatic flow of emotion in recall improves adaptive decision-making. Two studies examine the hypothesis that affect control in emotional recall facilitates inhibitory control and benefits long-term decision-making. In Experiment 1 (n = 137), affect control was assessed in emotional recall to examine if switching of affect in recall of positive and negative valence (order: positive-negative memory recall vs. negative-positive memory recall) is linked with long-term decision-making. Results for long-term decision-making showed that negative-positive recall sequence was associated with higher long-term decision-making, whereas automatic frequency-based decision-making remained unaffected by the recall sequence. In experiment 2 (n = 71, all male), emotional recall (positive vs. negative), recall specificity (i.e., specific vs. overgeneralized recall), and post-recall mood regulation (post-recall positive mood regulation vs. no regulation) was expected to facilitate long-term decision-making. Results showed that emotional recall and post-recall mood regulation (i.e., negative recall - positive mood and positive recall - negative mood) were associated with higher long-term decision-making (decks C' and D'). Results of frequency decision-making showed that positive emotional recall, and poor recall specificity led to infrequent punishment deck choices (decks B' and D'). Hierarchical regression indicated that emotional recall increased infrequent deck choices and accounted for 10% of choices made, recall specificity increased the explanatory power to 19%, and higher recall specificity was associated with fewer infrequent punishment deck choices. Affect control engaged via negative emotional recall, post-recall mood regulation, and recall specificity might be a potential mechanism through which affect control in emotional recall might facilitate long-term decision-making.

Thermodynamic and Dynamic Components of Winter Temperature Changes in Western Canada, 1950–2020

Abstract Most of the globe has experienced significant warming trends that have been attributed to anthropogenic climate change. However, these rates of warming are also influenced by short-term climate fluctuations driven by atmospheric circulation dynamics, resulting in inconsistent trend magnitudes in both time and space. This research evaluated winter (December–February) temperature trends over 1950–2020 at 91 climate stations across British Columbia (BC), Alberta (AB), and Saskatchewan (SK), Canada, and determined the components attributed to thermodynamic and dynamic (atmospheric circulation) factors. A synoptic climatological approach was used to classify atmospheric circulation patterns in the midtroposphere, relate those patterns to surface temperature, and evaluate changes in frequency. Moderate to high temperature increases over 71 years were found for most of the region, averaging 3.1°C in southern SK to 4.1°C in central-northern AB, and a maximum of 5.8°C in northern BC. Low to moderate increases were found for southern BC, averaging 1.2°C. Changes in atmospheric circulation accounted for 29% and 31% of observed temperature changes in central-northern BC and AB, respectively. Dynamic factors were a moderate driver in southern AB (18%) and central-northern SK (13%), and low in southern SK (5%). Negative dynamic contributions in southern BC (−6%), suggest that atmospheric circulation changes counteracted thermodynamically driven temperature changes. Results were consistent with trend analyses, indicating this method is well suited for trend detection and identification of thermodynamic and dynamic drivers. Results of this research improve our understanding of the magnitude of winter temperature changes critical for informing adaptation and climate-related policy decisions. Significance Statement Winter temperatures are strongly influenced by atmospheric circulation patterns, which move warm or cold air masses over large distances. We wanted to understand how changes in atmospheric circulation affect observed changes in winter temperatures in three provinces in western Canada. This also helps us to understand how much temperature change is due to anthropogenic (e.g., caused by greenhouse gases and land cover changes) and naturally occurring changes to Earth’s energy balance. Our results highlight the importance of understanding variability when selecting a time series for trend analyses or climate baselines for modeling studies. This study also helps to inform climate-related policies, decision-making, and adaptation strategies.

Five tensions in climate adaptation research

Climate change adaptation is a maturing field of research imbued with many complexities and tensions. In this article, we outline five tensions that we observe in our own adaptation research. These are between: adaptation as a research topic and practical challenge; uncertainty in adaptation research and decision-makers' desire for certainty; the global scope of adaptation research and its highly context-specific nature; the newness of climate adaptation research and its push to address old problems; adaptation as a specialization and the need for all researchers to engage. Our aim is to encourage critical discussion and reflection among researchers about how adaptation research is positioned within, shaped by and influences social and institutional settings. Given its emplaced character, adaptation research needs to attend to its content and context.

Gender relations and decision-making on climate change adaptation in rural East African households: A qualitative systematic review

Background: Climatic changes are threatening rural livelihoods in East Africa. Evidence suggests that climate change adaptation in this context might reproduce inequitable intra-household gender relations and that adaptation may be more effective when women are involved in meaningful ways. Hence, a nuanced understanding of the gendered nature of intra-household adaptation decision-making is essential for gender-responsive research, policy-making and practice. This qualitative systematic review aimed to investigate how gender relations influence decision-making concerning climate change adaptation in rural East African households and how decisions about climate change adaptation influence intra-household gender dynamics, in turn. Applying qualitative meta-synthesis principles, systematic searches were conducted in 8 databases and supplemented with comprehensive hand searches. 3,662 unique hits were screened using predetermined inclusion criteria, leading to a final sample of 21 papers. Relevant findings of these studies were synthesised using inductive thematic coding, memoing and thematic analysis. While men tended to be the primary decision-makers, women exercised some decision-making power in traditionally female domains and in female-headed households. Women’s and men’s roles in intra-household adaptation decision-making appeared to be influenced by a plethora of interconnected factors, including gender norms, gendered divisions of labour and access, ownership and control over resources. Intra-household adaptation seemed to impact the dynamics between male and female household members. The pathways of this influence were complex, and the ultimate outcomes for men and women remained unclear. We discuss our findings with reference to theoretical literature on gender-transformative approaches in development and adaptation and previous research concerning the gendered nature of climate change adaptation in East Africa. We then discuss implications for gender-responsive adaptation interventions.