Developing nations incur a greater risk to climate change than the developed world due to poorly managed human/natural resources, unreliable infrastructure and brittle governing/economic institutions. These vulnerabilities often give rise to a climate induced “domino effect” of reduced natural resource production-leading to economic hardship, social unrest, and humanitarian crises. Integral to this cascading set of events is increased human migration, leading to the “spillover” of impacts to adjoining areas with even broader impact on global markets and security. Given the complexity of factors influencing human migration and the resultant spill-over effect, quantitative tools are needed to aid policy analysis. Toward this need, a series of migration models were developed along with a system dynamics model of the spillover effect. The migration decision models were structured according to two interacting paths, one that captured long-term “chronic” impacts related to protracted deteriorating quality of life and a second focused on short-term “acute” impacts of disaster and/or conflict. Chronic migration dynamics were modeled for two different cases; one that looked only at emigration but at a national level for the entire world; and a second that looked at both emigration and immigration but focused on a single nation. Model parameterization for each of the migration models was accomplished through regression analysis using decadal data spanning the period 1960-2010. A similar approach was taken with acute migration dynamics except regression analysis utilized annual data sets limited to a shorter time horizon (2001-2013). The system dynamics spillover model was organized around two broad modules, one simulating the decision dynamics of migration and a second module that treats the changing environmental conditions that influence the migration decision. The environmental module informs the migration decision, endogenously simulating interactions/changes in the economy, labor, population, conflict, water, and food. A regional model focused on Mali in western Africa was used as a test case to demonstrate the efficacy of the model.
The current expansion of natural gas (NG) development in the United States requires an understanding of how this change will affect the natural gas industry, downstream consumers, and economic growth in order to promote effective planning and policy development. The impact of this expansion may propagate through the NG system and US economy via changes in manufacturing, electric power generation, transportation, commerce, and increased exports of liquefied natural gas. We conceptualize this problem as supply shock propagation that pushes the NG system and the economy away from its current state of infrastructure development and level of natural gas use. To illustrate this, the project developed two core modeling approaches. The first is an Agent-Based Modeling (ABM) approach which addresses shock propagation throughout the existing natural gas distribution system. The second approach uses a System Dynamics-based model to illustrate the feedback mechanisms related to finding new supplies of natural gas - notably shale gas - and how those mechanisms affect exploration investments in the natural gas market with respect to proven reserves. The ABM illustrates several stylized scenarios of large liquefied natural gas (LNG) exports from the U.S. The ABM preliminary results demonstrate that such scenario is likely to have substantial effects on NG prices and on pipeline capacity utilization. Our preliminary results indicate that the price of natural gas in the U.S. may rise by about 50% when the LNG exports represent 15% of the system-wide demand. The main findings of the System Dynamics model indicate that proven reserves for coalbed methane, conventional gas and now shale gas can be adequately modeled based on a combination of geologic, economic and technology-based variables. A base case scenario matches historical proven reserves data for these three types of natural gas. An environmental scenario, based on implementing a $50/tonne CO 2 tax results in less proven reserves being developed in the coming years while demand may decrease in the absence of acceptable substitutes, incentives or changes in consumer behavior. An increase in demand of 25% increases proven reserves being developed by a very small amount by the end of the forecast period of 2025.
To fill a major knowledge gap, Sandia National Laboratories (SNL) and the Electric Power Research Institute (EPRI) are jointly engaged in a multi-year research effort, supported by the Department of Energy’s SunShot Program, to examine real-world photovoltaic (PV) plant reliability and performance. Findings and analyses, derived from field data documented in the PV Reliability Operations Maintenance (PVROM) database tool as well as from convened workshops and working group discussions, are intended to inform industry best practices around the optimal operations and maintenance (O&M) of solar PV assets. To improve upon and evolve existing solar PV O&M approaches, this report: 1. Provides perspective on the concept of PV “system” reliability and how it can inform plant design, operations, and maintenance decisions that produce better long-term outcomes; 2. Describes the PVROM data collection tool, its technical capabilities, and results generated from database content in 2014; 3. Presents ongoing research efforts that are meant to drive the solar industry toward PV O&M best practice protocols and standards; and 4. Reflects on future areas of inquiry that can help better forecast plant health (e.g., system component availability, component wear out, etc.) and associated lifecycle costs. Ultimately, this report adds to the knowledge base of improving PV system O&M activities by discussing data collection and analysis techniques that can be used to better understand and enhance the reliability, availability, and performance of a photovoltaic system.
This paper is the output from SNL’s involvement in the Western Area Power Administration (WAPA), the Colorado River Energy Distributors Association (CREDA), and the Upper Colorado River Commission’s (UCRC) sponsored Phase II work to establish market and non-market values (NMV’s) of water and hydropower associated with Glen Canyon Dam (GCD) operations and the Colorado River ecosystem. It describes the purpose and need to develop a systems model for the Colorado River Basin that includes valuations in the economic, hydrologic, environmental, social, and cultural sectors. It outlines the benefits and unique features associated with such a model and provides a roadmap of how a systems model would be developed and implemented. While not meant to serve as a full development plan, the ideas and concepts herein represent what the Sandia National Laboratories (SNL) research team believes is the most impactful and effective path forward to address an ever increasing complex set of problems that occur at the basin-scale and beyond.
People save for retirement throughout their career because it is virtually impossible to save all you’ll need in retirement the year before you retire. Similarly, without installing incremental amounts of clean fossil, renewable or transformative energy technologies throughout the coming decades, a radical and immediate change will be near impossible the year before a policy goal is set to be in place. This notion of steady installation growth over acute installations of technology to meet policy goals is the core topic of discussion for this research. This research operationalizes this notion by developing the theoretical underpinnings of regulatory and market acceptance delays by building upon the common Technology Readiness Level (TRL) framework and offers two new additions to the research community. The new and novel Regulatory Readiness Level (RRL) and Market Readiness Level (MRL) frameworks were developed. These components, collectively called the Technology, Regulatory and Market (TRM) readiness level framework allow one to build new constraints into existing Integrated Assessment Models (IAMs) to address research questions such as, ‘To meet our desired technical and policy goals, what are the factors that affect the rate we must install technology to achieve these goals in the coming decades?’