Adaptation Policies Research Articles

Can coastal and marine carbon dioxide removal help to close the emissions gap? Scientific, legal, economic, and governance considerations

In this Policy Bridge, we present the key issues regarding the safety, efficacy, funding, and governance of coastal and marine systems in support of climate change mitigation. Novel insights into the likely potential of these systems for use in mitigating excess carbon dioxide emissions are presented. There may be potential for coastal blue carbon and marine carbon dioxide removal (mCDR) actions to impact climate change mitigation significantly over the rest of the 21st century, particularly post 2050. However, governance frameworks are needed urgently to ensure that the potential contribution from coastal and ocean systems to climate change mitigation can be evaluated properly and implemented safely. Ongoing research and monitoring efforts are essential to ensure that unforeseen side effects are identified and corrective action is taken. The co-creation of governance frameworks between academia, the private sector, and policymakers will be fundamental to the safe implementation of mCDR in the future. Furthermore, a radical acceleration in the pace of development of mCDR governance is needed immediately if it is to contribute significantly to the removal of excess carbon dioxide emissions by the latter half of this century. To what extent large-scale climate interventions should be pursued is a decision for policymakers and wider society, but adaptive legal, economic, policy, research, and monitoring frameworks are needed urgently to facilitate informed decision-making around any implementation of mCDR in the coming decades. Coastal and ocean systems cannot be relied upon to deliver significant carbon dioxide removal until further knowledge of specific management options is acquired and evaluated.

A unified approach to long-term population monitoring of grizzly bears in the Greater Yellowstone Ecosystem

Long-term wildlife research and monitoring programs strive to maintain consistent data collections and analytical methods. Incorporating new techniques is important but can render data sets incongruent and limit their potential to discern trends in demographic parameters. Integrated population models (IPMs) can address these limitations by combining data sources that may span different periods into a unified statistical framework while providing a holistic view of population dynamics. We developed an IPM in a Bayesian framework for grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem. We coupled demographic data with multiple, independent population count data to link annual changes in abundance with vital rates over 4 decades (1983–2023). Abundance increased threefold from an estimated 270 individuals in 1984 to 1030 individuals in 2023. Parameter estimates indicated survival of bears ≥2 years of age was high, contributing to robust population growth during the 1980s (λ = 1.023 [50 % interquartile range = 0.993–1.082]) and 1990s (λ = 1.064 [1.023–1.103]). A slowing of population growth started around 2000 (2000s: λ = 1.030 [0.989–1.068]) and continued into the 2010s (λ = 1.021 [0.985–1.057]), due primarily to reductions in survival of bears <2 years of age. These findings corroborate previous research that identified density-dependent effects as a likely cause. The IPM framework provided greater certainty and understanding regarding the dynamic demographic characteristics of the population and serves as a powerful monitoring tool for this long-lived species. Implementation of the IPM allows timely dissemination of demographic data to help inform adaptive management strategies and policy decisions necessary for the continued management and conservation of this population. This robust and flexible monitoring system allows scientists to investigate the effects of a changing ecosystem on population dynamics, incorporate new data sources and statistical models, and respond to changes in monitoring needs for the population. We highlight the efficacy of the IPM in estimating and tracking demographic parameters for a long-lived species, while accommodating shifts in monitoring techniques and data collections typical of long-term wildlife conservation programs worldwide.

Adapting to Climate Change: From Capitalism to Democratic Eco-Socialism

ABSTRACT The climate crisis is growing worse. Efforts to mitigate climate change have failed to stem the growing production of the greenhouse gas pollution that has created and exacerbated it. This failure is largely attributable to capitalism, including its current neoliberal form, and its continued addiction to fossil fuels. Consequently, there have been growing calls to ramp up efforts to help communities and countries, especially the world’s poorest, adapt to the inevitably worsening impacts of climate change. It is unlikely that adaptation policies premised on capitalism will be any more effective than capitalist-oriented mitigation policies. Instead, what is required is “transformational change” premised on alternative socio-economic assumptions that put environmental sustainability, social justice and economic equity before material growth and profit-seeking. Mainstream analyses of transformation rarely move beyond recommendations for changing prevailing ideas regarding nature, coupled with upscaling renewable energy, while slow-rolling the transition away from fossil fuels. This leaves untouched the prevailing institutions of national and global societies that are blocking a rapid path to sustainability and an effective response to climate change. In contrast, democratic eco-socialism offers a transformative alternative with much greater potential to enable fair and effective adaptation to the future impacts of climate change.

Recommendations for incorporating invasive species into U.S. climate change adaptation planning and policy

AbstractExecutive Order No. 14008 mandated that U.S. federal agencies and departments develop Climate Change Adaptation Plans to enhance the nation's resilience to climate change. Invasive species are documented to reduce the effectiveness of climate adaptation and mitigation actions. In spite of this, only eight of the 26 federal plans directly reference invasive species, and just four meaningfully consider the reciprocal impact of invasive species on climate adaptation efforts. To ensure that climate adaptation planning and processes are effective with associated benefits for biodiversity conservation, a gap analysis of the Climate Change Adaptation Plans was conducted. Five key recommendations were developed that would transform how invasive species are considered within them. Examples of how invasive species have been included in some of the plans are provided, with suggestions for agencies that did not include invasive species on how they might do so, to illustrate that dedicated efforts in prevention, detection, and management of invasive species can safeguard carbon sequestration, protect communities and infrastructure, and minimize economic and biodiversity losses. Although developed based on U.S. federal plans, these recommendations can be used by state and local, as well as international, jurisdictions tasked with implementing climate‐adaptive policies to build resilience across multiple sectors.

Affective Sensitivity to Air Pollution (ASAP): Person-specific associations between daily air pollution and affective states.

Individuals' sensitivity to climate hazards is a central component of their vulnerability to climate change. In this paper, we introduce and outline the utility of a new intraindividual variability construct, affective sensitivity to air pollution (ASAP)-defined as the extent to which an individual's affective states fluctuate in accordance with daily changes in air quality. As such, ASAP pushes beyond examination of differences in individuals' exposures to air pollution to examination of differences in individuals' sensitivities to air pollution. Building on known associations between air pollution exposure and adverse mental health outcomes, we empirically illustrate how application of Bayesian multilevel models to intensive repeated measures data obtained in an experience sampling study (N = 150) over one year can be used to examine whether and how individuals' daily affective states fluctuate with the daily concentrations of outdoor air pollution in their county. Results indicate construct viability, as we found substantial interindividual differences in ASAP for both affect arousal and affect valence. This suggests that repeated measures of individuals' day-to-day affect provides a new way of measuring their sensitivity to climate change. In addition to contributing to discourse around climate vulnerability, the intraindividual variability construct and methodology proposed here can help better integrate affect and mental health in climate adaptation policies, plans, and programs.

Localising the Global Goal on Adaptation through intersectional thinking

This viewpoint explores how intersectional thinking can help localise the Global Goal on Adaptation (GGA) to promote equity and justice in climate adaptation. Localising the GGA is essential for translating global objectives into actionable strategies at the local level. Adaptation goalsetting and current adaptation measurement approaches are hindered by the complexity of establishing shared metrics, the myopic focus on compliance and reporting, and the neglect of the dynamic, context-specific nature of adaptation processes. By incorporating intersectional thinking, we argue for a more nuanced approach that considers the compounded sources of vulnerability, especially in urban areas, where marginalised groups are disproportionately affected by climate impacts. We apply an Intersectional Climate Justice framework to guide the localisation of the GGA throughout the adaptation policy cycle, from impact, vulnerability, and risk assessment, to planning, implementation, and measurement. Embedding intersectional thinking throughout the GGA’s adaptation policy cycle aligns global goals with local realities, enabling local actors to develop more comprehensive, context-specific strategies that address the needs and priorities of vulnerable communities. We also highlight the potential of intersectional climate justice to bridge climate action and broader development goals, ultimately fostering more equitable and just futures.

The economywide recovery measures in Rwanda during the COVID-19 pandemic: How useful a lesson?

In response to the COVID-19 pandemic, governments in many low- and middle-income countries (LMICs) immediately introduced strict public health measures to contain the spread of infection. The economic consequences of these measures were negative and often large in magnitude, necessitating the rapid introduction of economic relief and recovery measures to reinvigorate economic growth and safeguard affected households. Rwanda stands out among other LMICs both for its effective public health policy response and its adaptive economic policy response to COVID-19. Using a dynamic computable general equilibrium (CGE) model of Rwanda, this paper assesses the economywide and welfare impacts of these policy measures and contextualizes results within the overall policy response to COVID-19. Modeling results generated at an early stage of Rwanda’s pandemic experience indicated that relief and recovery measures introduced in 2020 could mitigate COVID-19′s recessionary effects by about 0.8 % points of total GDP, help to recover about 115,000 jobs and, through cash transfers, significantly reduce income losses among poorer households, with total benefits from the measures exceeded their cost by a ratio of 2.2. These results demonstrate the widely replicable value of economywide modeling when used in a timely manner and when positioned within a policymaking process.

Assessing farmers’ knowledge of environmental policy along the Ayeyarwady River: Strides towards the Indian Ocean marine life safety

The Ayeyarwady River Basin in Myanmar is grappling with severe environmental challenges, including soil erosion and water pollution, primarily driven by unsustainable agricultural practices. This study aims to evaluate farmers' awareness of environmental policies and identify barriers to their effective implementation. In-depth interviews were conducted with 45 stakeholders, encompassing farmers, government officials, and researchers. The findings highlight a significant lack of policy awareness among farmers, exacerbated by socio-cultural, economic, and institutional obstacles. These barriers impede the successful application of environmental policies, perpetuating environmental degradation. The study advocates for integrative strategies that encompass education, community engagement, and adaptive policy frameworks to address these complex issues. Detailed policy implications are provided, offering insights into potential solutions for enhancing the region's environmental governance and sustainable development. This research contributes to understanding the critical interplay between policy awareness and ecological management, underscoring the importance of targeted interventions to mitigate environmental threats.

Impact of Monetary Policy on Stock Market Volatility

Purpose: The general objective of the study was to analyze the impact of monetary policy on stock market volatility. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of monetary policy on stock market volatility. Preliminary empirical review revealed that monetary policy exerts significant influence on stock market volatility through various channels such as interest rate adjustments, quantitative easing, and forward guidance. It highlighted that the timing and clarity of policy announcements play a crucial role in shaping market reactions, with unexpected shifts often leading to heightened volatility. Global interconnectedness amplified these effects, underscoring the need for coordinated policy responses. Behavioral factors also contributed, as investor sentiment and market psychology could magnify volatility independently of economic fundamentals. Understanding these dynamics is crucial for effective policy design and market stability amidst evolving financial landscapes. Unique Contribution to Theory, Practice and Policy: Efficient Market Hypothesis (EMH, Rational Expectations Theory and Keynesian Economics may be used to anchor future studies on monetary policy on stock market volatility. Recommendations from the study included integrating behavioral finance models into economic frameworks to enhance predictive accuracy. It advocated for clearer and more consistent central bank communications to align market expectations with policy actions, reducing uncertainty-driven volatility. Collaborative efforts among central banks were emphasized to manage international spillovers and enhance global financial stability. Adaptive policy frameworks were proposed to respond flexibly to changing market conditions while promoting regulatory measures to safeguard against excessive risk-taking. These recommendations aimed to foster a stable financial environment supportive of sustainable economic growth and market confidence.

Climate Change and Ecosystem Services: A Participatory Approach in a Brazilian Mountainous Region

Climate change is present in all sectors of global societies, causing various damages, including to agriculture. In this process, understanding the perception of the most affected population and including them in decision-making is a matter of social justice and a more assertive path. Aiming to obtain subsidies for better adaptation to climate change in family farming, this study established a methodology for evaluating the risks of climate change impacts on ecosystem services, related to water and food security, under the perception of rural actors in the mountainous region of the Rio de Janeiro state, Brazil. The methodology consisted of five steps, including the application of the questionnaire to 29 rural actors and ending with validation of the results in a focus group. The main results obtained were that the interviewees perceive the risks of the impacts of climate change in ES, both related to water and food security. Additionally, 24.14% of interviewees mentioned that their family has a monthly income below the Brazilian minimum wage, which exposes their food insecurity. In relation to water security, the risks classified as Very High were mainly in relation to low per capita investments in adaptation policies and infrastructure for environmental protection, between others. The use of many chemical inputs in agriculture also highlighted as a risk to water and food security. Regarding the methodology, it concluded that it was effective in obtaining the perception of the rural actors interviewed regarding the impacts of climate change on ecosystem services at a local scale.

The soft channels of policy diffusion: Insights from local climate change adaptation policy

AbstractDiffusion has become both an important concept for studying policy spread and a popular governance approach, particularly where direct coercion is unavailable or undesirable. However, the prevailing mechanism‐centered concept is difficult to measure and poorly captures the governance potential of policy diffusion. To address these issues, this article presents a new channel‐centered framework that distinguishes between six soft policy diffusion channels: autonomous, collaborative, exemplary, persuasive, organized, and funded diffusion. The framework is probed by studying local climate change adaptation policy using original survey data collected from the administrations of 190 municipalities located in the central German state of Hessen. The regression results indicate that the local institutionalization of adaptation in Hessen is associated with several interventions by higher levels of government, including the provision of a policy model, a municipal climate network, and grant programs. However, the density of concrete adaptation measures is associated with noninstitutionalized exchanges between municipalities. External grants are also found to be more effective in institutionalizing adaptation in larger municipalities. These results demonstrate the usefulness of the framework for distinguishing and comparing different diffusion channels and suggest that different types of interventions may be required to effectively support adaptation policy development at the local level.

Assessment and strategies for water supply security risks in the estuarine city Shanghai under normalized extreme climate conditions

Facing the escalation of extreme climate events, estuarine delta cities like Shanghai grapple with significant water supply challenges. This study employs system dynamics and dynamic adaptive policy pathways to assess Shanghai's water supply security risks and responsive strategies in 2022–2050, amidst extreme climate conditions. Utilizing data from 2000 to 2021, we constructed a system dynamics model to predict future water demand under various development modes. Focusing on the unusual 2022 drought in the Yangtze River Basin, we simulated 15 scenarios, including economy, population, water efficiency, and reservoir levels, to identify the extent and timing of potential water supply risks, then proposed pertinent dynamic adaptive strategies to address them. Our findings suggest that the 2022 drought significantly reduced Shanghai's water supply capacity, leading to a notable deficit. Under scenarios of accelerated economic growth, water supply security risks are heightened, with projections indicating a reduction of days of supply available to merely 33–67 days, and escalating water shortage amount to 592–896 million m3 by 2050. Short-to-medium-term recommendations include optimizing both local and transit water resources, strengthening emergency water reserves, enhancing water use efficiency, and maintaining stable reservoir water levels. For the long term, expanding water storage infrastructure and promoting integrated water resource management within the Yangtze River Delta is key to establishing a resilient and diversified water supply system, effectively mitigating future water security risks. This study provides a scientific basis and reference for the sustainable management of water resources in estuarine cities confronting normalized extreme climate conditions. It offers valuable insights for policymakers and actionable suggestions for urban planners.

Analyzing effect modifiers of the temperature-mortality relationship in the Paris region to identify social and environmental levers for more effective adaptation to heat

Adaptation to heat is a major challenge for the Paris region (France). Based on fine-scale data for the 1,287 municipalities of the region over 2000–2017, we analyzed (time-serie design) the temperature-mortality relationship by territories (urban, suburban, rural), age (15–64 and ≥ 65) and sex, and explored how it was modified by vegetation and socio-economic indicators. Heat was associated with an increased mortality risk for all territories, age groups, sex, and mortality causes. Women aged 65 and over residing in the most deprived municipalities had a relative risk (RR) of deaths at 29.4 °C (compared to 16.6 °C) of 4.2 [3.8:4.5], while the RR was 3.4 [3.2:3.7] for women living in less deprived municipalities.Actions to reduce such sex and social inequities should be central in heat adaptation policy.

Pengaruh Migrasi terhadap Konsep Kewarganegaraan di Negara-Negara Maju

This study aims to analyze the influence of migration on the concept of citizenship in developed countries. Adopted the literature review method by investigating 10 journal articles published between 2000 and 2019. This research aims to understand how international migration flows affect the definition and practice of citizenship. Migration between countries in the era of globalization is something that cannot be prevented or avoided. Motives underlying the movement of labor between countries or international migration. The first motive, they work abroad with the aim of selling their energy, skills or cleverness. Usually the main stream of labor flows of this motive comes from developing countries to developed countries, or from poor countries to rich countries, or from labor-surplus countries to countries experiencing labor shortages. The second motive is that they work abroad in connection with the sale of technology or capital investment. The mainstream of this second motive is generally from developed countries to developing countries. These findings underscore the need for more inclusive and adaptive policy adjustments to deal with the social changes resulting from migration.

Decision-focused neural adaptive search and diving for optimizing mining complexes

Optimizing industrial mining complexes, from extraction to end-product delivery, presents a significant challenge due to non-linear aspects and uncertainties inherent in mining operations. The two-stage stochastic integer program for optimizing mining complexes under joint supply and demand uncertainties leads to a formulation with tens of millions of variables and non-linear constraints, thereby challenging the computational limits of state-of-the-art solvers. To address this complexity, a novel solution methodology is proposed, integrating context-aware machine learning and optimization for decision-making under uncertainty. This methodology comprises three components: (i) a hyper-heuristic that optimizes the dynamics of mining complexes, modeled as a graph structure, (ii) a neural diving policy that efficiently performs dives into the primal heuristic selection tree, and (iii) a neural adaptive search policy that learns a block sampling function to guide low-level heuristics and restrict the search space. The proposed neural adaptive search policy introduces the first soft (heuristic) branching strategy in mining literature, adapting the learning-to-branch framework to an industrial context. Deployed in an online fashion, the proposed hybrid methodology is shown to optimize some of the most complex case studies, accounting for varying degrees of uncertainty modeling complexity. Theoretical analyses and computational experiments validate the components’ efficacy, adaptability, and robustness, showing substantial reductions in primal suboptimality and decreased execution times, with improved and more robust solutions that yield higher net present values of up to 40%. While primarily grounded in mining, the methodology shows potential for enabling smart, robust decision-making under uncertainty.

A resilient scheduling framework for multi-robot multi-station welding flow shop scheduling against robot failures

With the development of intelligent manufacturing, robots are being increasingly applied in manufacturing systems due to their high flexibility. To avoid production disruptions caused by robot failures, higher requirements are imposed on the resilience of systems, specifically in terms of resistance, response, and recovery capabilities. In response to this, this paper investigates the resilient scheduling framework for multi-robot multi-station welding flow shop, thereby endowing and enhancing the resilience of the system. Within the resilient scheduling framework, a proactive scheduling method maximizing resistance capability is firstly proposed based on an improved NSGA-III with variable neighborhood search. Secondly, to improve the response and recovery capabilities of the system, a recovery scheduling method is presented. Therein, an adaptive trigger policy based on deep reinforcement learning is introduced to enhance the rapid response capability for disturbances, while the recovery optimization grants the system the ability to recover its performance that has been degraded due to the impact of disturbances. Finally, through simulation experiments and case study, it is verified that the proposed algorithms and framework possess superior performance of multi-objective optimization, which can endow the multi-robot multi-station welding flow shop with resilience to against uncertain robot failures.

Enhancing equity of the post-disaster recovery governance through community data generation

Climate adaptation and disaster risk reduction policy frameworks emphasize the importance of engaging vulnerable communities in data creation and utilization to enhance resilience. Yet, empirical evidence on how capacity strengthening and community data generation can improve post-disaster recovery processes remains limited. To address this gap, this article investigates the post-disaster recovery process in the El Pacífico self-built neighborhood, located at the hillslopes of the rural-urban border of Medellín, Colombia. Utilizing participatory action research and documentation analysis, we examined documents related to the response and recovery committee (called “Mesa de Atención y Recuperación”) established following a severe flooding event. Our analysis reveals a lack of clearly defined procedures for engaging affected communities in the post-disaster efforts. However, we provide evidence that initiatives promoting equitable governance through capacity strengthening and community data generation, supported by our project, have effectively empowered community members, amplifying their voices in the negotiation with municipal authorities. These findings offer valuable insights that can inform more equitable future policies and practices in disaster risk reduction and climate adaptation with increased recognition and representation of marginalized urban residents.

Renewable energy allocation in Multi-Owned Buildings for Sustainable Transition: A novel Evidence-Based Decision-Making framework

Adopting Renewable Energy Systems (RES) in Multi-Owned Buildings (MOBs) is critical for achieving sustainability goals, yet the equitable allocation of energy from a jointly-owned RES to individual apartments remains overlooked in practice and literature. Current practices, rooted in models of common cost allocation, fail to address the dynamic traits of energy allocation and disregard the energy entitlement of each apartment, necessitating a tailored approach for renewable energy allocation in MOBs. This paper emphasises energy entitlement and introduces a novel, evidence-based decision-making framework assessing nine distinct energy allocation models for their suitability in diverse MOB typologies, characterised by physical and social factors, and presents a ranked list. Our findings reveal extensive variation in model suitability depending on the building typology. Equal energy allocation minimised financial disparities, while demand-based allocation was significantly effective for older, mid-rise buildings with fewer tenants. Conversely, the flat-fee model was found unsuitable regardless of building type. Furthermore, the study demonstrates that the suitable model for a building typology may not always align with the objectives of RES installation, thus endorsing an ‘objective proximity’ analysis. The proposed framework serves as a valuable guide for stakeholders, including the owners’ corporations, policymakers, and industries, to make well-informed decisions for a smooth transition to renewable energy. It lays the foundation for potential expansion to other building types while underscoring the necessity for adaptive policies in promoting RES adoption.

Mitigating the disparities in carbon emission efficiency enhancement in China's coal resource-based cities

Enhancing carbon emission efficiency in coal resource-based cities (CRBCs) is pivotal for energy conservation and carbon reduction, but the combined pressures of technical efficiency and technological innovation may drive a sharp disparity in the sustainability of carbon emission efficiency enhancement across such cities. Here, we develop a “Static-Dynamic-Synergistic” progressive assessment framework, to systematically assess the carbon emission efficiency of 51 CRBCs in China from 2000 to 2020. We found that the carbon emission efficiency of CRBCs exhibited a fluctuating upward trajectory, with the average efficiency rising from 0.40 to 0.64. The enhancement of carbon emission efficiency in CRBCs has shifted from relying solely on efficiency change (EC) to incorporating both EC and technology change (TC). TC, EC, and the Carbon Emission Efficiency Improvement Synergy Index (CEEISI) exert a substantial positive influence on the increment of carbon emission efficiency. While the CEEISI of maturity CRBCs has increased by 2.22 % over the past 15 years, varying declines are observed for other types of CRBCs. CRBCs should formulate adaptive policies based on the stage of city development, fostering socio-economic-ecological transformation, synergistically improve technical efficiency and technological innovation, thereby achieving sustainable enhancement in carbon emission efficiency.

Weaving scientific and local knowledge on climate change impacts in coastal Kenya, Western Indian Ocean

Climate change poses severe threats to coastal social-ecological systems (SES) worldwide. Recent calls recognize the importance of including Indigenous and local knowledge (ILK) in research on climate change impacts. Yet studies that have attempted to weave ILK and scientific knowledge have seldom considered the gendered nature of climate change impacts. Building on the literature on gender and climate change and knowledge pluralism, this study contributes to addressing this research gap by exploring local knowledge on climate change impacts and its relation to scientific knowledge through a gendered approach and focusing on the Western Indian Ocean region, and more specifically on Kenya. We adopted a mixed methodology combining qualitative and quantitative approaches. We found evidence of pronounced climate change impacts on coastal SES both in the scientific literature and in local reports. Our findings highlight that there is an extensive overlap between information derived from scientific and local knowledge systems. Importantly, our study revealed reports of change that were only provided by SSF communities, namely changes in coastal dynamics, a decrease in rainfall, and a decrease in the abundance of green algae. Although we found gendered variations in changes reported by SSF communities, gendered differences of climate change impacts on SSF were not detected in the reviewed literature. Overall, our results suggest that knowledge cross-fertilization generates a holistic, relational, and place-based view of climate change impacts, which may support sound and gender-inclusive adaptive policies. We conclude by suggesting key policy recommendations for climate adaptation and risk management