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Recent advances in systematic conservation planning make use of modern portfolio theory (MPT)—a framework to construct and select optimal allocation of assets—to address the challenges posed by climate change uncertainty. However, these methods are difficult to implement for fine‐scale conservation planning when the information on future climate scenarios is insufficient. Insufficient information makes the estimators of the key inputs in the optimisation procedure unreliable leading to technical problems for the construction of optimal asset allocation.We identify three statistical methods—constant correlation model, the Ledoit–Wolf approach and the weighted non‐negative least‐squares approach—that can overcome the lack of sufficient information and enable the use of MPT for fine‐scale conservation planning.We illustrate the use of the three methods for identifying efficient portfolio allocation strategies, that is, strategies that give minimum amount of risk for a chosen level of return or maximum return for a chosen level of risk, using case studies of wetland conservation planning in North America and coastal conservation planning in Australia. We compare conservation planning strategies with complete information using standard portfolio theory and with insufficient information using the three methods to highlight their advantages and disadvantages. We find the Ledoit–Wolf and weighted non‐negative least‐squares approaches perform well and can identify risk‐return outcomes that are close to those identified with complete information.The methods presented in this study broaden the range of cases where the application of MPT is possible in conservation planning to enhance its uptake and lead to more efficient allocation of conservation resources. [ABSTRACT FROM AUTHOR]

Background: In recent years, demand response (DR) has gained increased attention from utilities, regulators, and market aggregators to meet the growing demands of electricity. The key aspect of a successful DR program is the effective processing of data and information to gain critical insights. This study aims to identify information processing needs and capacity that interact to improve energy DR effectiveness. To this end, organizational information processing theory (OIPT) is employed to understand the role of Information Systems (IS) resources in achieving desired DR program performance. This study also investigates how information processing for DR systems differ between developing (India) and developed (Australia) countries. Method: This work adopts a case study methodology to propose a theoretical framework using OIPT for information processing in DR systems. The study further employs a comparative case data analyses between Australian and Indian DR initiatives. Results: Our cross case analysis identifies variables of value creation in designing DR programs - pricing structure for demand side participation, renewable integration at supply side, reforms in the regulatory instruments, and emergent technology. This research posits that the degree of information processing capacity mediates the influence of information processing needs on energy DR effectiveness. Further, we develop five propositions on the interaction between task based information processing needs and capacity, and their influence on DR effectiveness. Conclusions: The study generates insights on the role of IS resources that can help stakeholders in the electricity value chain to take informed and intelligent decisions for improved performance of DR programs. [ABSTRACT FROM AUTHOR]

Recent climate change has contributed to shifts in the seasonal interplay between precipitation and potential evapotranspiration, which have in turn increased droughts and reduced freshwater availability in Mediterranean climate regions. To overcome limitations in existing indices for comparing these seasonal hydroclimatic drivers at the global scale, we introduce an information theory‐based, nonparametric asynchronicity index that captures both the temporal alignment and relative magnitudes of precipitation and potential evapotranspiration. We use this asynchronicity index to first identify Mediterranean climates around the world. We then apply the asynchronicity index over two Mediterranean climate regions and show that their boundaries have shifted between 1960 and 2018, resulting in a regional expansion in the U.S. Pacific Northwest and a contraction in southwestern Australia. These results highlight the need for globally consistent measures of seasonal climatic water supply and demand for diagnosing potential changes in water resources and ecosystem responses within Mediterranean climate regions. Plain Language Summary: Climate change is likely to change the boundaries of what are typically considered Mediterranean climate regions, which have dry summers and mild, wet winters. To analyze how these regions' water availability may be influenced by climate change, we introduce a metric for quantifying the mismatch in the timing and amount of precipitation relative to atmospheric water demand. This new metric is able to identify the boundaries of Mediterranean climate regions more effectively than other existing indices and can be used to analyze how these boundaries shift over time. Key Points: A new metric is proposed to measure the seasonal mismatch between atmospheric water supply (precipitation) and demand (potential evapotranspiration, PET)Compared to existing metrics, the proposed metric can more accurately distinguish between climates with synchronized versus desynchronized seasonal signals of precipitation and PETMediterranean climates are characterized by high climate asynchronicity, and their geographical extents have expanded in the U.S. Pacific Northwest and contracted in Western Australia since the turn of the century [ABSTRACT FROM AUTHOR]