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This paper investigates the impacts of economic (in)stability on carbon emissions and the role of renewable energy in these effects at the crossroads of accelerated climate change and rising global economic instability. The paper hypothesizes that these implications should be examined not by direct impacts across variables, but rather via indirect effects serially mediated by renewable energy consumption and economic growth. This paper analyzes the United States from January 1980 and April 2022 using manifest structural equation modeling. The paper reaches two major conclusions. First, there are clear distinctions between (i) the direct and indirect effects of economic stability, surrogated by inflation and policy interest rate, on carbon emissions, (ii) the symmetric and asymmetric indirect effects, and (iii) the roles of aggregate renewable energy and its components, such as biomass and hydropower energy use, in mediating these symmetric and asymmetric indirect effects. Second, renewable energy significantly reduces the carbon-increasing effects of inflation and interest rates. The paper contains proper policy suggestions based on the findings. [ABSTRACT FROM AUTHOR]

This paper collects monthly data for the United States from January 2005 to April 2021 and develops a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model to investigate the time-varying relationship between climate policy uncertainty, oil prices, and renewable energy consumption. In addition, this paper discusses the dynamic impact of climate policy uncertainty on five types of renewable energy consumption. Our results show that the relationship between them is time-varying. In most periods, climate policy uncertainty positively affects oil prices in the short and medium term, and positively affects total renewable energy consumption in the short and long term. In addition, the impact of climate policy uncertainty on the consumption of the five types of renewable energy is heterogeneous. The results of the robustness tests indicate that our results are robust and our study has important implications for the development of relevant policies. [ABSTRACT FROM AUTHOR]

This paper evaluates the impact of climate policy uncertainty on renewable and non-renewable energy consumption in the United States over the quarterly data from 2000Q1 to 2021Q3. Economic growth and crude oil prices are added to the energy consumption functions as control variables. The paper considers several approaches to model both renewable and non-renewable energy demand. It is found that crude oil prices promote non-renewable energy demand and climate policy uncertainty reduces it. Surprisingly, the impact of economic growth on non-renewable energy consumption is positive but insignificant. It is also observed that economic growth promotes renewable energy demand, and crude oil prices reduce it. Furthermore, climate policy uncertainty positively affects renewable energy demand in the long run. Some policy implications are provided for reducing non-renewable energy consumption and promoting renewable energy use in the United States through climate policy implementation. • We examine the impact of the Climate Policy Uncertainty (CPU) on renewable and non-renewable energy consumption. • We use the quarterly data from 2000Q1 to 2021Q3 in the United States. • The CPU reduces non-renewable energy demand. • The CPU positively affects renewable energy demand in the long run. • Promoting renewable energy use through climate policy implementation is discussed. [ABSTRACT FROM AUTHOR]

The debate about the threshold effect of sectoral renewable energy consumption on economic growth has attracted extreme attention, but only limited evidence supports this issue. Therefore, the objective of this paper is to study the non-linear relationship between economic growth, capital, labor and sectoral renewable energy consumption for the case of the United States during the period 1981Q1 and 2021Q1. Using the threshold detection method proposed by Hansen (2002), the effect of sectoral renewable energy consumption on economic growth is positive and significant only if the electric power, industrial, residential, commercial, and transport sectors exceed a renewable energy consumption threshold lnREC >7.095; lnREC >6.138; lnREC >4.897; lnREC >3.212; and lnREC >2.849, respectively. Indeed, for the U.S. to achieve positive economic growth from its renewable energy investments, it must exceed a certain threshold of renewable energy consumption for each sector. However, if the United States use renewable energy below a certain threshold, the effect of sectoral renewable energy consumption on economic growth is negative. These results have important implications for both policymakers and investors. [Display omitted] [ABSTRACT FROM AUTHOR]

Wave energy is one of the most concentrated ocean renewable energy resources. Although wave energy has been studied extensively for more than four decades, there is no large commercial installation for wave energy production or a consensus framework on how to exploit this resource. Wave energy is a complex resource that directly depends on two meteorological parameters, which produced significant fluctuations of wave energy in both temporal and spatial criteria. This paper presents a new concept called Energy Event, to analyze meteorological data generated by WaveWatch III over 36 years in the U.S. to characterize and assess wave energy behavior using the peak-over-threshold methodology. This methodology used extreme statistics, segmented the wave energy with different thresholds, and assessed wave energy production on a temporal and spatial framework. Three areas were studied in this paper, including the Gulf of Mexico, the East and West U.S. Coasts. The results indicated that wave energy behaved as a two-state energy system with each state having independent characteristics. The main difference among the three studied areas was the constant baseline of wave power density, with the West Coast having the highest constant baseline and the Gulf of Mexico having the lowest baseline. [ABSTRACT FROM AUTHOR]

The benefits of biomass as an alternative fossil fuel can be realized mainly through biomass consumption. This paper examines the growth rate and spatial patterns of biomass consumption in the United States (hereafter US) during the period 2000–2012, using the SLOPE index, Moran's I and local Moran's I. Results indicate that US States show either slow growth or slow decline in biomass consumption during the period 2000–2012. The number of States that have slow growth in biomass consumption is slightly larger than the number of States showing slow decline. The pattern of growth rate of biomass consumption revealed by the SLOPE index and the observed pattern of the temporal variation of biomass consumption ensures that the SLOPE index can catch the growth rate of biomass consumption. This pattern does not align with the increased annual investment in biomass, indicating that the investment in biomass does not flow down to the biomass industry effectively. The pattern of growth rate of biomass consumption will provide additional evidence for the debates about the effectiveness of State renewable energy policy, and demonstrates an urgent need for appropriate State renewable energy policies. There is a High-High spatial cluster around the Southeast of the US and a Low-Low spatial cluster in the Middle of the US in 2000, 2004, 2008 and 2012. The regional spatial cluster in biomass consumption has somewhat relationship with the growth rate of biomass consumption, which can help the understanding of the growth rate of biomass consumption. • The SLOPE index is used to investigate the growth rate of biomass consumption. • The pattern of growth rate does not align with the increased annual investment. • There is an urgent need for appropriate State renewable energy policies. • There are regional clusters in biomass consumption in 2000, 2004, 2008 and 2012. • The regional spatial cluster has somewhat relationship with the growth rate. [ABSTRACT FROM AUTHOR]

Bio-power and biofuels are promising alternative energy resources. This paper investigates their role in the long-term U.S. national energy and transportation portfolio planning, while considering the competition among other energy options. The paper presents a systematic modeling framework for integrating biomass pathways to the energy and transportation systems, and also captures the geographical variation in the feedstock availability and cost across the U.S. The paper then presents two different case studies-energy sector planning and integrated energy & transportation sectors planning. The studies reveal long-term cost and emission savings from bio-renewables, where the bulk of benefits are observed due to biofuels (with bio-power production limited by feedstock prices). Under a 40% CO 2 emissions reduction scenario over the next 40 years, penetration of bio-renewables promise up to 10-Trillion USD (2010$) savings in system costs (investments and operational). Simulations also show that the impediment with bio-renewable penetration is mostly influenced by the availability of low-cost feedstock, specifically for bio-power production. According to current estimation of long-term feedstock availability, U.S will be able to power upto 150 Billion Gallons Year (BGY) (or approx. 560*10 9 L per year) bio-refinery capacity around 2020s, and about 200 BGY (or approx. 750*10 9 L per year) by 2050. [ABSTRACT FROM AUTHOR]

The ocean plays host to a vast array of sources of energy. These energies simply need to be developed efficiently and effectively, as well as harmonized and collected. This paper will present and discuss two major sources of sustainable ocean energy—wind and wave—which can be combined effectively into one multi-purpose structure. These dynamic structures are emerging as useful in many international settings. However, the United States' (U.S.) areas are slow to adopt. This research presents a hierarchical analysis for decision makers to utilize in strategic offshore location planning with regard to these platforms. Surprisingly, the Gulf of Mexico (GOM) proves to be in a favorable position for entities considering use of such multi-purpose platforms. • Combinatory analysis of energy collection modes. • Provides a conceptual framework using a hierarchical analysis tool. • Optimal location decisions for U.S. coastal regions. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a refined description of the wave climate observed in a Portuguese location for marine energy projects. The data used in this case study were derived from 3-month in situ measurements carried out using three directional buoys that have been deployed simultaneously – yet independently (various buoy types and water depths) – on the Atlantic coast in the neighbourhood of Figueira da Foz. Two ways of reporting the statistics were adopted. In the first one, each recorded sea state was summarized as a set of global parameters accounting for energy, mean frequency and direction, spectral bandwidth and directional spreading related to the main ongoing wave field considered as unimodal. In the second one, each sea state has been decomposed into its own wave components – swells and wind-sea – which are characterised separately and individually by the same set of parameters as previously. Besides the adopted data processing techniques and the illustration of such thorough wave climate description modes, the paper also addresses the advantages and limits of each adopted parameterization in the frame of simultaneous and independent in situ wave measurements. [Copyright &y& Elsevier]

This paper investigates the demand determinants of green power in the U.S. residential sector. The data employed were collected by the National Renewable Energy Laboratory and consist of a cross-section of seven utilities observed over 13 years. A series of tests are performed that resulted in estimating a demand equation using the one-way cross-section random effects model. As expected, we find that demand is highly price inelastic. More interestingly though, is that elasticity with respect to number of customers is 0.52 leading to the conclusion that new subscribers tend to purchase less green power on average than the existing customers. Another compelling finding is that obtaining accreditation will have a 28.5% positive impact on consumption. Knowing that gaining green accreditation is important to the success of programs, utilities may want to seek certification and highlight it in their advertising campaigns. [ABSTRACT FROM AUTHOR]

Abstract: In the United States, electricity consumers are told that they can “buy” electricity from renewable energy projects, versus fossil fuel-fired facilities, through participation in voluntary green power markets. The marketing messages communicate to consumers that they are causing additional renewable energy generation and reducing emissions through their participation and premium payments for a green label. Using a spatial financial model and a database of registered Green-e wind power facilities, the analysis in this paper shows that the voluntary Renewable Energy Certificate (REC) market has a negligible influence on the economic feasibility of these facilities. Nevertheless, voluntary green power marketers at least implicitly claim that buying their products creates additional renewable energy. This study indicates the contrary. Participants in U.S. voluntary green power markets associated with wind power, therefore, appear to be receiving misleading marketing messages regarding the effect of their participation. In the process of completing this analysis, a potentially relevant factor in explaining investor behavior was identified: the potential for the overlap of voluntary REC markets with compliance REC markets that supply utilities need to meet their obligations of Renewable Energy Portfolio Standard (RPS). The majority of state RPS rules allow for regional or even national sourcing of RECs, meaning that projects are generally eligible to provide compliance RECs to utilities not only in their home states, but in several other states. [Copyright &y& Elsevier]

Abstract: Renewable energy resources have historically played a small role for electricity generation in the US. However, concerns such as security of energy supply, limitations and price fluctuations of fossil fuels, and threats of climate changes have encouraged US policy makers to think and debate about diversification strategy in the energy supply and promotion of renewables. The current paper discusses the role of renewable portfolio in the US energy action plan during 2010–2030. A system dynamics model is constructed to evaluate different costs of renewable energy utilization by 2030. Results show that while renewables will create a market with near 10 billion $ worth (in the costs level) in 2030, the total value of renewable energy promotion and utilization in the US will be more than 170 billion $(in the costs level) during 2010–2030. [Copyright &y& Elsevier]

Abstract: Natural disasters are increasing in frequency and cost throughout the United States. Long term power outages frequently result from natural disasters, which leads to higher reliance on inefficient and cost ineffective gasoline or diesel powered generators to meet energy needs. The development of deployable renewable energy-powered microgrids as mobile power sources would allow energy demands to be met in portable and effective way, while reducing diesel fuel consumption. Characterizing system performance of renewable energy-powered microgrids prior to deployment would allow a future system to be appropriately sized to meet all required electrical loads at a given intermittent diesel generator operational frequency. Appropriate sizing of renewable energy powered microgrids and backup diesel generators would decrease system operation and transportation costs as well as define the appropriate amount of fuel to be kept on hand. This paper focuses on developing figures that represent the quantity of external AC or DC load a microgrid could supply as a function of intermittent diesel generator operational frequency. Typical meteorological year 3 (TMY3) data from 217 Class I locations throughout the United States were inserted into an operational frequency prediction model to characterize the quantity of external AC and DC load the system could supply at intermittent diesel generator operational frequencies of 1%, 5%, 10%, 25%, and 50%. Ordinary block Kriging analysis was performed to interpolate AC and DC load power between TMY3 Class I locations for each diesel generator operating frequency. Figures representing projected AC and DC external load were then developed for each diesel generator operating frequency. [Copyright &y& Elsevier]

Abstract: Wind energy development is booming worldwide with Germany and the United States being forerunners in installed capacity. Based on a review of relevant laws and regulations, policy, siting, and permitting documents, academic literature, and expert interviews, the paper compares the permit and environmental impact assessment (IA) policies and regulations pertaining to wind energy projects in both countries. The focus is on the potential of IAs in supporting an environmentally conscious development of wind power generation. This includes the analysis of IA regulations on the project level, strategic level impact assessment and planning processes, opportunities for public involvement and transparency of processes, and the clarity and predictability of the IA provisions. The findings suggest certain variations between both countries'' permit and IA processes for wind energy projects and further research needs, inter alia on the actual effects of strategic assessment of wind development on subsequent permitting. [Copyright &y& Elsevier]

Abstract: Biofuel production from renewable biomass has experienced increased interest in order to reduce U.S. dependence on imported oil and reduce carbon emissions. Concerns regarding cost efficiency and environmental problems result in significant challenges that hinder the increased biofuel production from renewable biomass. This paper describes the development of a simulation model with an easy-to-use graphical user interface for the biofuel supply chain. The model considers key supply chain activities including biomass harvesting/processing, transportation, and storage. The model uses the delivered feedstock cost, energy consumption, and GHG emissions as system performance criteria. The utility of the supply chain simulation model is demonstrated by considering a biomass supply chain for a biofuel facility in the lower peninsula of Michigan. The results show that the model is a useful tool for supply chain management, including selection of the optimal biofuel facility location, logistics design, inventory management, and information exchange. [Copyright &y& Elsevier]

Abstract: Consumer perception could play an important role in the adoption of renewable energy technologies. This study aims to explore the role of consumer acceptance and model its effect on residential photovoltaic (PV) adoption. A survey was conducted to understand consumer perceptions of the technology (perception variables), such as perceived cost, perceived maintenance requirement, and environmental concern. To further investigate the adoption potential of residential PV, this paper develops a fuzzy logic inference model to relate consumer perception variables (inputs to the model) to their purchasing probability (output from the model). This model is tested in a case study of residential PV adoption using data from a survey of homeowners in Arizona, United States. The quantitative results of the model demonstrate the role of each perception variable in the consumer acceptance of PV. Public has tended to emphasize on the role of cost reduction in promoting the adoption of residential PV. The results of this study show that other issues such as maintenance requirement and environmental concern are also important. [Copyright &y& Elsevier]

Abstract: Biomass is expected to become an important energy source in U.S. electricity generation under state-lead renewable portfolio standards. This paper investigated the greenhouse gas (GHG) emissions for energy generated from forest resources through pyrolysis-based processing. The GHG emissions of producing pyrolysis bio-oil (pyrolysis oil) from different forest resources were first investigated; logging residues collected from natural regeneration mixed hardwood stands, hybrid poplar cultivated and harvested from abandoned agricultural lands, short rotation forestry (SRF) willow plantations and waste wood available at the site of the pyrolysis plant. Effects of biomass transportation were investigated through a range of distances to a central pyrolysis facility through road transport by semi-truck. Pyrolysis oil is assumed to be converted to electrical power through co-combustion in conventional fossil fuels power plants, gas turbine combined cycle (GTCC) and diesel generators. Life cycle GHG emissions were compared with power generated using fossil fuels and power generated using biomass direct combustion in a conventional Rankine power plant. Life cycle GHG savings of 77%–99% were estimated for power generation from pyrolysis oil combustion relative to fossil fuels combustion, depending on the biomass feedstock and combustion technologies used. Several scenario analyses were conducted to determine effects of pyrolysis oil transportation distance, N-fertilizer inputs to energy crop plantations, and assumed electricity mixes for pyrolysis oil production. [ABSTRACT FROM AUTHOR]

Abstract: Wind is one of the most promising sources of alternative energy. The construction of wind farms is destined to grow in the U.S., possibly twenty-fold by the year 2030. To maximize the wind energy capture, this paper presents a model for wind turbine placement based on the wind distribution. The model considers wake loss, which can be calculated based on wind turbine locations, and wind direction. Since the turbine layout design is a constrained optimization problem, for ease of solving it, the constraints are transformed into a second objective function. Then a multi-objective evolutionary strategy algorithm is developed to solve the transformed bi-criteria optimization problem, which maximizes the expected energy output, as well as minimizes the constraint violations. The presented model is illustrated with examples as well as an industrial application. [Copyright &y& Elsevier]

Abstract: During the summer of 2003, the state of Arizona took delivery of a set of high-resolution wind energy maps that were developed with a meso-scale wind energy model coupled with wind data. The geographical information system data supplied with the wind maps was used to create a wind resource inventory that included wind energy potential, proximity to transmission lines, and land ownership. Four diverse sites were selected for further study, one predominantly class 3, one predominantly class 4, one predominantly class 5, and one predominantly class 6. At each site, the capacity factor was determined, the seasonal influence was observed, and the real levelized cost of energy in 2005 dollars determined. As the wind class varied from 6 to 3 the levelized cost of energy ranged from 4.22 to 6.00cents/kWh. These results do not include the production tax credit or the renewable energy production incentive, do include adjustments for elevation, losses, and inflation, and are considered conservative. This paper documents the findings of the wind mapping process, describes the method and results of evaluating the most promising sites for wind development, and presents the cost of energy results. [Copyright &y& Elsevier]