Adaptive management and environmental decision making: A case study application to water use planning

Opening a policy window for organisational change and full-cost accounting: The creation of BC Hydro's water use planning program

The aim of the study was to investigate the application and importance of Learning and Adaptive Management by the Ministry of Women Affairs, Community, Small and Medium Enterprises Development (MWACSMED) and beneficiaries when implementing development programs in Zaka district. The study investigated the objectives of the programs implemented by MWACSMED in Zaka district, the extent to which learning, risk management and adaptive management mechanisms are used by MWACSMED Zaka district are effective. The study also examined the challenges faced MWACSMED and beneficiaries with development programs in Zaka district. The study also aimed at developing a model for learning, risk management, adaptive management, monitoring, and evaluation for the efficient and effective implementation of development programs and the Monitoring, Evaluation, Learning, Adaptive and Risk Management model (MELARM) was developed. The researcher chose to conduct his research in this area because Zaka district is one of the districts in the country that is heavily reliant on subsistence farming, with farmers lacking adequate rain and inputs. Furthermore, there is no other industry or activity in the district that contributes to people's well-being. According to Chingarande and Matondi (2020:3), Zaka district is in a province that is reliant on activities such as livestock production, irrigated farming, wildlife conservation and smallholder agricultural production to support its population. Zimbabwe, where Zaka district is in the province of Masvingo used to be one of Southern Africa’s most vibrant, productive, and resilient countries, but the country has been facing economic and political challenges for close to two decades linked to decades of poor governance and corruption. Approximately 63% of Zimbabwean households are living in poverty and 16% in extreme poverty (USAID Country Development Cooperation Strategy 2016-2021:4). Given the above challenges in Zimbabwe, it is important to investigate whether decisionmakers are investing in long-term management approaches such as Adaptive Management. Decision-makers tend to be reluctant to invest in long-term management approaches which cover the concept of Adaptive Management and choose small-scale experiments instead. Moreover, they do not utilize opportunities to adjust and make improvements in their approach to management. Managers have often been making decisions based on funding opportunities that are short-term and political requests, rather than using the actual objectives of a program. The study used the quantitative approach in the form of a questionnaire that was designed using Google forms and shared through a link that was send to research participants through emails and WhatsApp. The sample population for the research questionnaire was 10 officials from MWACSMED and 7 beneficiaries of the programs managed by the Ministry in Zaka district. As shown by the study, the Ministry has not yet fully recognized the use of Learning and Adaptive Management to address the complexity and uncertainty of development programs. The Ministry has been managing programs through traditional monitoring and evaluation systems. The results of the study show that the Ministry was not using Adaptive Management approaches or learning thorough monitoring and evaluation systems, which may be because of socio-economic and political reasons. The introduction of adaptive programming and management calls for using information and learning to inform adjustments during implementation, which requires monitoring and evaluation systems that go beyond reporting on pre-specified indicators The study made recommendations for Learning and Adaptive Management mechanisms that are essential for development projects. The study also drafted a model known as the Monitoring, Evaluation, Learning, Adaptive and Risk Management Framework (MELARM). These recommendations are all based on the findings of the study. In general, government departments should avoid traditional thinking about program cycles, and they should merge program design, implementation, and evaluation through Learning and Adaptive Management. Government departments/agencies should also identify and document how procurement, contracting, work planning, reporting and risk management can foster adaptive programming and management to achieve intended development goals.

The nature and peculiarities of formation of risks of economic activity of enterprise are researched. Expediency of application of instruments of adaptive risk management of economic activity of enterprise on the basis of use of the situational approach and study of the risk management system in dynamics is substantiated. A model of adaptive risk management of economic activity of enterprise on the basis of precedents has been formed, which includes subsystems of active and reactive risk management. Important components of influence on the adaptive management system are the current risk situation caused by the existing risk factors, the dynamics of risk factors development and the strategy of risk management of economic activity adopted by the enterprise, which defines the targets, priorities and prerequisites for making adaptive management decisions at the enterprise in the face of risk. The accumulation of information about the object of management within this model allows developing optimal management decisions, accumulating information about adaptive decisions in the Bank of precedents of management decisions and using them as a template for making management decisions in similar (typical) situations. It is substantiated that an important feature of the development of the system of adaptive management of economic risks at enterprise is the presence of a certain direction of transformation - the vector of development of the adaptive management system. A vector model of adaptive transformation of the risk management system at enterprise has been proposed, which contributes to a comprehensive understanding of the process of development of the system of adaptive risk management of economic activity of enterprise. Building a model of risk management at enterprise on the basis of precedents, taking into account the vector nature of its development, will contribute to a holistic understanding of the patterns of formation and transformation of the system of adaptive risk management of economic activity at enterprise. The study substantiates the feasibility of using an adaptive approach in the system of enterprise economic risk management, which is considered as a flexible open system capable of self-learning and progressive development. The use of precedents for making adaptive management decisions increases the system's ability to self-develop and more effectively adapt to the changing conditions of the external and internal environment of the enterprise.

This paper addresses issues on adaptive water management under the impact of climate change. Based on a set of comprehensive indicators of water system, a decision making approach of multi-objects is developed and applied to quantify water adaptive management for the demands of water sustainable use, water environmental protection and eco-water requirement under the climate change. For this study in China, two key indicators are proposed, namely (1) the water resources vulnerability (V) that was represented by integrated sensitivity (S) and resilience (C) of climate change impact on water resources, and (2) the sustainability of socio-economy and water environment, marked by DD, that is integrated scaler of socio-economic development (EG) based on the amount of GDP and the water environment and relative eco-system quality (LI). To find a reasonable solution for adaptive water management, a multi-objective decision making model of adaptive water management is further developed and the multi-objective model was transformed into an integrated single optimization model through developing an integrated measure function, called as VDD=DD/V. This approach has been applied to adaptive water resources planning and management for case study of China with new policy, called as the strict management of water resources based on three red line controls, i.e., the control of total water use by the total water resources allocation, the control of lower water use efficiency by the water demand management and the control of the total waste water load by water quality management in the Eastern China Monsoon Region that covers major eight big river basins including Yangtze River, Yellow River, Haihe River and Huaihe River. It is shown that the synthetic representation of water resource vulnerability and socio-economic sustainability by the integrated objective function (VDD) and integrated decision making model are workable and practicable. Adaptive management effect of the criterion compliance rate and water use efficiency are more appreciable through new water policy of the three red line controls, which can reduce 21.3% of the water resources vulnerability (V) and increase 18.4% of the sustainability of socioeconomy and water environment (DD) for the unfavorable scenario of climate change in 2030.

Guided adaptive optimal decision making approach for uncertainty based watershed scale load reduction

Facilitating adaptive management in a government program: A household energy efficiency case study

Monitoring and evaluation for adaptive coastal management

Marine hydrokinetic (MHK) energy offers a promising new source of renewable ocean energy. However, the young industry is faced with significant challenges. Most notable is the challenge of regulatory uncertainty that is thought to hamper the successful deployment of new tidal energy technologies. Adaptive management may be one approach to deal with uncertainty and inform permitting decisions for hydrokinetic projects. In this study, we apply the concept of adaptive management to the Cobscook Bay Tidal Energy Project in Maine to better understand and inform permitting decisions. Using a social science approach of observation, interviews, and document analysis, we examine (1) agency roles and authority, (2) agency interactions, (3) regulatory change, and (4) challenges faced in the regulatory and permitting process for MHK development at the federal and state level. We found four institutional factors favorable to an adaptive approach. These include experimentation and learning, institutionalized choice to correct avoidable error, a strong commitment to interagency coordination, and an emphasis on early proactive engagement with project developers. We also identified institutional challenges or vulnerabilities. These include conflicting agency cultures, high financial costs, and long timeframes associated with baseline data collection. Lessons learned from this study can assist regulators, policymakers, and project developers design and implement an actively adaptive management approach that can move new renewable ocean energy development forward in a way that is socially acceptable and environmentally responsible.

Long-term, flexible therapy strategies are needed for chronic diseases like cardiovascular ailments, diabetes, and chronic kidney disease and they have to deal with patient heterogeneity, changing physiological states and privacy constraints. This research suggests a framework for privacy-preserving and personalized artificial intelligence that combines Federated Learning (FL), a Res-HyperTransformerNet deep prediction model, Personalized Multi-Agent Reinforcement Learning (PMARL) and Explainable Artificial Intelligence (XAI) for adaptive chronic disease management. The framework is structured with two main aims: (i) to create an accurate and privacy-aware predictive model for chronic disease risk using diverse data sources, and (ii) to permit adaptive, individual therapy optimization through multi-agent reinforcement learning. Federated learning is utilized to conduct the training of Res-HyperTransformerNet over distributed Internet of Medical Things (IoMT) nodes without the requirement of transferring raw patient data. The embeddings that are created from patient data are then passed on to a PMARL module, where several agents optimize therapy dimensions like medications, diet, physical activity, and mental health interventions independently. To make the clinical support more understandable, the SHAP-based explainability method is used for both predictive and decision-making parts. The framework is tested using two public datasets—the CDC Chronic Disease dataset and the UCI Chronic Kidney Disease Risk Factor dataset. The performance is measured by employing classification metrics (accuracy, precision, recall, F1-score, MCC) as well as reinforcement learning metrics (reward score, convergence steps, episode return) and federated system metrics (communication overhead, convergence rounds, and training time). The experiments indicate that the proposed framework demonstrated improved performance in terms of predictive accuracy and policy convergence speed compared to the baseline deep learning and reinforcement learning models, and at the same time it is more cost-effective when it comes to communication in a federated setting. This means that the proposed method suggests the potential of being a reproducible and open AI framework for adaptive chronic disease therapy management. The combination of ResNet and Transformer blocks, i.e., Federated Res-HyperTransformerNet, achieved strong performance in both datasets, namely CDC Chronic Disease (Dataset 1) and UCI CKD Risk Factor (Dataset 2). The obtained accuracy for Dataset 1 was 98.61% and for Dataset 2, it was 97.75%, under the conducted experimental settings.

From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning (RL), a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where RL holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable and discuss technical and social issues that arise when applying RL to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises and perils of experience-based decision-making. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Climate change demands new forms of environmental decision-making. The concept of adaptive management can contribute to this transformation. Adaptive management recognises the dynamism of natural systems and the importance of monitoring, review, and modification of projects, plans and activities in response to new understanding. As the dominant approach in natural resource management, it finds remarkably little explicit reflection in legal frameworks. Five key mechanisms are advocated by which to implement adaptive management in law: changing statutory objectives; requiring monitoring and evaluation of projects, plans and activities; staged approvals processes; conditional approvals and statutory triggers; and proportionate resource allocation models. Wider use of these flexibility mechanisms would enable environmental decision- making to respond to the impacts of climate change, while continuing to provide a level of legal certainty. Their uptake requires shifts in the institutional culture of administering agencies and the assumptions underpinning current approaches to environmental and resource management law.

Quantifying the contribution of habitat dynamics relative to intrinsic population processes in regulating species persistence remains an ongoing challenge in ecological and applied conservation. Understanding these drivers and their relationship is essential for managing habitat‐dependent species, especially those that specialize in transitional habitats. Limitations in the ability of natural disturbance to mediate transitional habitat dynamics have resulted in a decline in early‐ and mid‐successional vegetation structure and prompted the need for aggressive habitat management to replace natural perturbations and increase habitat structural complexity. We describe a collaborative effort with a group of independent land managers to design an adaptive management program for restoring an imperiled ecosystem and recovering declining populations of an endemic habitat specialist. We developed a set of integrated, hierarchical models to estimate management‐mediated transition rates among vegetation classes in two dominant scrub communities and the species response (local colonization and extinction probabilities) as a function of habitat state. Models were fit using a long‐term data set of habitat and occupancy observations from 361 Florida scrub‐jay territories across two Florida counties. Occupancy model results correspond closely to previous approaches of estimating differential survival and reproductive success associated with habitat conditions, with highest colonization and lowest extinction rates estimated for those habitat states found to have the highest rates of survival and reproduction. In addition to offering an innovative approach for jointly modeling habitat and species population dynamics, the program we describe will also be of interest from a management perspective by providing guidance for developing collaborative, adaptive management frameworks from the ground up. We engaged land managers via workshops to specify objectives and desired state‐variable conditions, identify management alternatives, and elicit consensus opinions on model parameters. Treating expert opinions as pseudo‐observations to define Dirichlet priors allowed us to make use of existing management knowledge. Formal learning was then accumulated by updating transition probability estimates as management activities were implemented over the study period. We believe this adaptive management framework provides a useful approach for increasing our understanding of complex ecological relationships and hope that it will be adopted by others who have interest in informing management and conservation efforts.

Constructing the License to Operate: Internal Factors and Their Influence on Corporate Environmental Decisions

Voluntary programs intended to improve corporate environmental practices have proliferated in recent years. Why some businesses choose to participate in such voluntary programs, while others do not, remains an open question. Recent work suggests that companies’ environmental practices, including their decisions to participate in voluntary programs, are shaped by a license to operate comprised of social, regulatory, and economic pressures. Although these external factors do matter, by themselves they only partially explain business decision making, since facilities subject to similar external factors often behave differently. In this article, we draw from organizational theory to explain why we would expect a company's license to operate to be ultimately constructed by internal factors, such as managerial incentives, organizational culture, and organizational identity, as these shape both interpretations of the external pressures and organizational responses to them. Using qualitative data from an exploratory study of matched facilities that reached different decisions about participating in a prominent voluntary environmental program, we then report evidence indicative of the role of these internal factors in shaping facilities’ environmental decisions. Finally, we offer suggestions for future research that could further develop understanding of how internal organizational characteristics influence environmental management decisions, including those concerning participation in voluntary programs.

This paper focuses on the participation of, or consultation with, local people affected by a project It uses case studies of road planning as examples, placing particular emphasis on approaches to consultation. It is based on social assessments of road proposals with further commentary on the planning of water use for a major river in New Zealand. The paper also covers aspects of mediation as one approach to resolving conflicts, which arise during project planning, in a non-adversarial way.