Spatial Impact of Transportation Infrastructure: A Spatial Econometric CGE Approach

Transportation infrastructure plays an important role in regional economic development both in the stimulation of growth and as a response to output expansion. However, measuring these effects quantitatively has been a challenge due to the complicated impact mechanisms of transportation infrastructure. This complication is due to two reasons: first, regional impacts of transportation infrastructure are achieved through a mechanism that involves both a demand influence through the variation of transportation price and a supply influence implemented through the variation of transportation cost; second, impacts of transportation are usually evaluated in a regional context where the presence of unobserved local or regional variables may give rise to spatial autocorrelation. As a result, impact analysis may become biased and spurious. This study develops a new method called Spatial Econometric Computable General Equilibrium (SECGE) model, which integrates both spatial econometrics with equilibrium modeling techniques to improve the effectiveness of impact analysis on transportation infrastructure. This study differs from previous studies in the following three aspects: First, through a spatial autocorrelation test, the presence of spatial dependence is observed and confirmed among the elasticities of factor substitution in the US. To deal with spatial dependence, spatial panel econometric techniques are introduced to estimate the elasticity of factor substitution of different sectors for the Constant Elasticity of Substitution (CES) production function with consideration of spatial direct and indirect effects. Second, transportation impact analysis is conducted under different scenarios of general equilibrium frameworks. Unlike partial equilibrium analysis, general equilibrium analysis allows researchers to obtain much comprehensive understanding of transportation infrastructure?s impacts given its consideration of interactions between the demand and the supply. The study validates the method by comparing traditional equilibrium simulation without controlling for spatial dependence and the new equilibrium simulation with consideration of spatial dependence. The comparison allows researchers to justify spatial impacts of transportation infrastructure. Third, the study is conducted with a focus on multiple modes of transportation that includes road, rail, air, transit, pipeline and water transportation. Unlike a unimodal perspective, the perspective of multiple modes is essential to achieve a comprehensive understanding of the transportation infrastructure systems. It also enables us to compare impacts between different types of infrastructures and understand the relative importance of transportation investment by mode. The study confirms that the US highway and streets plays a dominant role among all transportation infrastructure systems in economic development while public transit and passenger transportation only plays the least important role among the systems. The result general equilibrium analysis also shows a difference between using spatial econometric estimates and traditional OLS estimates. Although the differences are relatively small in this aggregate case study, implications for more sensitive disaggregated regional models are clear

Optimizing Energy Storage Participation in Emerging Power Markets

The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving.Comment: The full (longer and extended) version of the paper accepted in IGSC 201

Optimizing energy storage participation in emerging power markets

The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving

Modeling of Macroeconomics by a Novel Discrete Nonlinear Fractional Dynamical System

We propose a new nonlinear economic system with fractional derivative. According to the Jumarie’s definition of fractional derivative, we obtain a discrete fractional nonlinear economic system. Three variables, the gross domestic production, inflation, and unemployment rate, are considered by this nonlinear system. Based on the concrete macroeconomic data of USA, the coefficients of this nonlinear system are estimated by the method of least squares. The application of discrete fractional economic model with linear and nonlinear structure is shown to illustrate the efficiency of modeling the macroeconomic data with discrete fractional dynamical system. The empirical study suggests that the nonlinear discrete fractional dynamical system can describe the actual economic data accurately and predict the future behavior more reasonably than the linear dynamic system. The method proposed in this paper can be applied to investigate other macroeconomic variables of more states

The Coupling Coordination Degree Measurement of Society-Economy-Ecosystem of Regional National Forest Park in Heilongjiang Province

In order to estimate the comprehensive benefits brought by forest parks to the society, economy, and ecology of a certain area, this paper innovatively constructed a social-economic-ecological composite system of forest parks in Heilongjiang Province. The entropy method is used to determine the weight of each index, the coupling coordination degree model is used to analyze the coupling and coordination degree of the social, economic, and ecological benefits of forest parks in Heilongjiang Province from 2010 to 2018. In addition, the LSTM neural network model is used to predict the development trend of the coupling coordination degree of the composite system from 2019 to 2021. Research shows that from 2010 to 2018, the forest park composite system was in a state of "high coupling and low coordination" for a long time; from 2019 to 2021, it is predicted that the degree of coupling of the composite system will decrease slightly and the degree of coordination will increase