Evaluation of electric vehicle response capability in power grid.

In recent years, frequent and substantial area-wide power outages have underscored the critical need for cities to possess robust backup power sources capable of swift response to prevent prolonged power system disruptions. Electric vehicles can contribute electricity to the power grid using vehicle-to-grid technology. The power delivered by electric vehicles in this context is termed as response capability. However, existing studies have overlooked response capability dynamics during transitions between electric vehicle states-such as the shift from charging or discharging to an idle state, thereby hindering a comprehensive understanding of this aspect. Hence, this paper introduces a multi-timescale response capability prediction model that evaluates the electric vehicle's state of charge to ensure users' requirements are met for upcoming trips. To better assess users' travel demand, the gravity model is employed as a precursor to response capability prediction to further enhance the validity of the prediction outcomes. Three neighborhoods in Los Angeles have been chosen for analysis: Downtown, Lincoln Heights, and Silver Lake. Predictions indicate that neglecting the response capability when electric vehicles undergo state transformation can lead to a differential response capability ranging from 2000 kWh to 4000 kWh, resulting in a loss of prediction accuracy by 20 % to 25 %.•The response capability of EV is non-zero during state transformations•Users' travel demand assessment•Seamless integration of vehicle-to-grid technology into the power grid.

Competitiveness of citrus export and its determinants: a two-way fixed effect panel data model approach.

Pakistan has a conducive condition for the development of a wide range of scrumptious fruits. As a result, the country grows a diverse assortment of tropical and subtropical fruits; the most prized and top-ranked fruit among all fruits grown in Pakistan is citrus. Citrus is the principal fruit that contributes significantly to Pakistan's export earnings and national income. In this study, the cross-border determinants influencing Pakistan's citrus exports to its topmost 22 trading partners are examined using a gravity model technique. This is the first large study from Pakistan by using gravity model to check the impact of various cross-border factors on citrus fruit export. The analysis is based on a panel dataset covering the years 2003 to 2021. To estimate the results, the study used fixed effect regression with time and country fixed effects. The results signify that per capita income, population, and some regional dummies are positively associated with citrus exports from Pakistan. Citrus price, distance, exchange rate, and other regional dummies are observed to have an adverse effect on citrus exports. Trade agreements between Pakistan and trade partners such as free trade agreements, preferential trade agreements, and SAFTA, have been observed as important determinants of citrus exports. Citrus exporters in Pakistan can also benefit from understanding the factors that influence export markets. By addressing the challenges identified in this study, Pakistan can enhance its citrus exports and boost its agricultural sector.

Exploring drivers of overnight stays and same-day visits in the tourism sector.

We employ mobile network data referred to the area of Lombardy in Italy to investigate alternative touristic behaviours, such as same-day visits and overnight stays in Italy. We show that larger availability of tourism accommodations, cultural and natural endowments are relevant factors explaining overnight stays. Conversely, temporary entertainment and transportation facilities increase municipalities attractiveness for same-day visits. The results also highlight a trade-off in the capability of municipalities of being attractive in connection to both the tourism behaviours. For instance, higher tourists arrivals are observed in areas receiving limited visitors, coming from municipalities with low same-day visits outflows. We highlight mobile data offer an adequate level of spatial and temporal granularity and can be thus employed to support policy makers in the design of effective tourist management strategies.

[Evolution and Influencing Factors of Spatial Correlation Network of Construction Carbon Emission in China from the Perspective of Whole Life Cycle].

Based on the whole life cycle perspective, the carbon emissions of the provincial construction industry in China from 2011 to 2019 were calculated from the production, construction, operation, and demolition stages of building materials. A spatial correlation network matrix of the carbon emissions in the construction industry was constructed by using the modified gravity model, and the structural characteristics of the correlation network were described by introducing social network analysis. Through the quadratic assignment program, the spatial correlation matrix of carbon emissions in the construction industry and its influencing factors were regressed and analyzed. The conclusions were as follows:① the spatial correlation network of carbon emissions in China's construction industry clearly existed. The network density and network correlation numbers were gradually rising, and the network tightness and stability were gradually improving. ② Shanghai, Tianjin, Beijing, and Jiangsu had a higher degree centrality and closeness centrality, which are the core and dominant positions of the spatial correlation network of carbon emissions in the construction industry. Zhejiang replaced Shanghai in the top four from 2013 to 2018, and the betweenness centrality of each province had unbalanced characteristics. ③ Beijing, Tianjin, Jiangsu, Inner Mongolia, Shanghai, and Shandong were "net beneficiaries" blocks, receiving the carbon emissions from other regions. Four provinces, Guangdong, Chongqing, Fujian, and Shandong, belonged to the "broker" sector, achieving a dynamic balance between the production and consumption sides of building carbon emissions. The remaining 20 provinces played a "net spillovers" role, actively sending carbon emissions from the construction industry to other provinces. The correlation between blocks was much greater than the correlation relationship within the blocks. ④ Industrial structure, urban population, spatial adjacency, consumption level, and construction industry process structure had a significant influence on the spatial correlation of carbon emissions in the construction industry. The greater the inter-provincial differences in industrial structure, urban population, spatial adjacency, and consumption level, the greater the similarity of inter-provincial construction industry process structure, and the stronger the spatial correlation and spatial spillover of the construction industry carbon emissions. Finally, according to the evolution characteristics and influencing factors of the spatial correlation network of building carbon emissions, relevant countermeasures and suggestions were provided for the collaborative carbon reduction development of the construction industry region.

Industrial and textile waste trade: Multilayer network and environmental policy effects.

Waste management is an international enterprise, and it is important to understand global flows of recyclable materials. The Pollution Haven Hypothesis (PHH) suggests that waste moves from high income nations with stringent environmental policy to low income nations with less environmentally stringent policy, by exploiting low labor and regulatory costs. This paper assesses the PHH thesis for slag/dross and textiles (SDT) wastes in PHH through novel integration of the multilayer network and gravity models. The multilayer network model generates network effect that quantifies interlayer connections of multiple waste trade networks. Instead of North-South movement of waste, North-North, South-South, and even South-North are shown. Results from the gravity model indicate that stringent waste management policies reduce both waste exports and imports. PHH is not found for slags/dross where high income countries are importing the waste, contradicting PHH. On the other hand, PHH is more evident between highly connected hubs and havens in SDT waste trade networks.

Spatiotemporal evolution and multi-scale coupling effects of land-use carbon emissions and ecological environmental quality.

The coupling relationship between land-use carbon emissions (LCE) and ecological environmental quality (EEQ) is critical for regional sustainable development. Rapid urbanization promotes a notable increase in LCE, which imparts significant stress on EEQ. This study used land use and cover change (LUCC) and Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) data from the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) to evaluate LCE, applied a remote sensing ecological index (RSEI) model to calculate EEQ, and combined gravity and centroid movement trajectory models to analyze the spatiotemporal evolution characteristics of LCE and EEQ. Four-quadrant and coupling degree (CD) models were used to analyze the synergistic relationship and interaction intensity between LCE and EEQ based on three different scales of pixels, counties, and cities. The results show that: (1) LCE and EEQ exhibit clear spatial inequality distribution, and the total amount of LCE increased from 40.16 Mt. in 2000 to 131.99 Mt. in 2020; however, LCE has not yet reached peak carbon emissions. (2) From 2000 to 2020, cities with a strong correlation between LCE and EEQ showed an increasing trend, and the centroid of LCE moved sharply to Jiangxi during 2000-2005 and 2005-2010. (3) High-CD areas were primarily located in quadrant II, and low-CD areas in quadrant IV. The relationship between LCE and EEQ has improved over the past 21 years, and CD has been increasing. (4) The stability of the coupling results between LCE and EEQ was affected by different research scales; the larger the research scale is, the greater the change in the results. This study provides a scientific basis and practical scheme for LCE reduction, ecological environmental management, and regional sustainable development in the UAMRYR.

Symphony of sustainability: how technology similarity impacts international trade patterns in environmental goods.

The global consensus on sustainable development and environmental cooperation has prompted the promotion of trade in environmental goods (EGs) for green growth. This study delves into the diversity of EGs trade patterns, using decomposed environmental technology similarity indicators to reveal the technological drivers. Linking innovation indicators to trade performance in EGs provides new insights into the determinants of inequality in environmental governance cooperation. Based on an extended gravity model, we empirically analyze their impact on bilateral EGs trade flows among 176 countries over the period 2002-2019. The study finds that (1) the global EGs trade network presents a "core-periphery" structure, with increased network density and participation of developing countries. (2) Technology similarity contributes significantly to EGs trade. Compared to competition, technology complementarity has a greater impact on EGs trade flows. (3) The influence of technology similarity varies across trade patterns and product complexity explains the mechanisms, with technology complementarity promoting more trade in high-complexity products, mainly concentrated in the trade from Northern countries, while technology competition greatly promotes the export of low-complexity products from the South. (4) Technology similarity helps to overcome information barriers in EGs trade, and its trade-enhancing effects exhibit geographical regionalization. The findings offer empirical evidence on the technological drivers of EGs trade and provide policy implications for fostering inclusive global environmental governance and enhancing the competitive advantages of Southern countries, fostering more opportunities for green growth.

Global energy transition and foreign environmental regulations: Impacts on Russian regions' export volumes.

This paper analyses the impacts of trading partners' environmental regulation on the export volumes of the Russian regions, using panel data covering about 140,000 observations in 84 Russian regions and 204 countries for the period 2013-2020. We employ the Gravity Model of international trade and Heckman Sample Selection methodology, with the fixed effects and random effects Poisson Pseudo-Maximum Likelihood techniques to analyse two subsamples of the Russian regions with different shares of environmentally sensitive goods in regional exports. As a robustness check, we utilize the non-parametric Driscoll-Kraay approach. The results of the econometric estimation demonstrate that environmental regulations enhance the export of regions with a high share of environmentally sensitive goods in export structure, while for the rest of the regions the environmental regulations negatively affect export volumes. The promoting effect is associated with a higher level of innovativeness of the regions and the role of the global energy transition process. We conclude that the environmental agenda is a challenge for some Russian regional exporters due to the lack of own environmental regulation, and to the specific features of the commodity and geographic structure of exports. To transform the negative impact into the positive one for all Russian regions, policy measures are necessary on the federal, regional, and company levels, encompassing environmental regulations and green development strategies, along with innovation and cluster policies, and the development of human capital and infrastructure.

Investigating the simultaneous impact of infrastructure and geographical factors on international trade: Evidence from asian economies.

Infrastructure and geography are the most important components of international trade. They provide trade-oriented amenities for the trade volume among trading partners. Thus, this study assesses the simultaneous impact of infrastructure and geographic factors on trade in Asian economies from 2004 to 2020. This work contributes to the existing literature by exploring the significance of infrastructure and geography in international trade. Furthermore, this research seeks to determine whether these factors have complementary or simultaneous effects. To examine these aspects, the augmented gravity model and cross-sectional autoregressive distributed lags are used in the current model. Then, the multilateral resistance terms are corrected. Results reveal that infrastructure has a significant and positive impact on trade. More precisely, transport infrastructure, communication infrastructure, financial infrastructure, and border-transport efficiency are productive influencers for trade over a certain period. Notably, the simultaneous impacts of infrastructure and geographic factors can lead to the deterioration of trade volume. The policy implications and future research direction of this study suggest that economies should improve infrastructure and geographic factors through gross domestic product.

Assessing the Seafood Trade Diversion Arising from Economic Sanctions: Evidence from Russia and Western Countries.

Since 2014, economic sanctions between Russia and Western nations have significantly altered the global seafood trade. The consequent decline in bilateral trade also had spillover effects on the rest of the world (ROW). According to earlier studies, economic sanctions appear to negatively impact bilateral trade and income. However, few studies examine how Russian sanctions affect the world as a whole and estimate their effects on the fisheries industry. This study seeks to close this gap by quantifying the extent to which Russian sanctions have impacted trade in terms of trade deflection, trade destruction, trade depression, and trade creation. To this end, panel data from 185 countries were created for the years from 2005 to 2020. With trade policy variables that account for changes in trade channels, a structural gravity trade model was specified. Based on calculations using the Poisson pseudo-maximum likelihood (PPML) fixed effect model, economic sanctions led to a 119.28% surge in Russia's seafood imports from the rest of the world (ROW), alongside a 39% decline in imports from Western countries. The extent of trade deflection, which includes the exports of Western nations diverted from Russia to the ROW markets, increased by 5.49%. The results demonstrate that trade between sanctioned states, as well as global trade, is significantly impacted by economic sanctions.

Does the digital economy empower the green development of foreign trade?

The digital economy has become a new driving force for China's economic development in recent years, and it may contribute to solving the corresponding problems of resource and environmental pressure. Therefore, this study strives to explore whether the digital economy contributes to the green development of foreign trade. First, this study measures the digital economy development in China. Second, this study presents a list of environmental and polluting products and examines the impact of digital economy development on exports of representative products based on an extended gravity model. The main findings are as follows. First, the digital economy significantly promotes the green development of foreign trade. Second, the signing of Free Trade Agreements (FTAs) and the income level of importing countries and regions strengthen the role of the digital economy in promoting the green development of foreign trade. Third, the digital economy promotes the green development of foreign trade through green technological innovation. Finally, some policy implications are proposed based on the findings of this study.

A Method for the Precise Coordinate Determination of an Inaccessible Location.

Surveyors are occasionally tasked to with determining the coordinates of inaccessible locations or points in civil engineering applications, ground control points for photogrammetry or LiDAR data acquisition, among others. The present work outlines and investigates a novel method for estimating the GNSS coordinates of an inaccessible location where a surveying instrument cannot be set up. The procedure is based on the well-known surveying intersection method and data extracted from an Earth Gravity Model (e.g., EGM 2008). The location's coordinates are obtained from the least-squares adjustment of the angles and distances measured from at least two sites to the unknown point using a total station, within the framework of the Gauss-Helmert method. Field tests confirmed that the accuracy of the determined coordinates of the inaccessible point is at the level of 1 cm. The proposed method bypasses standard coordinate transformation steps performed with the traditional approach, directly producing geocentric coordinates of the unknown points.

Investigation of spatial association network features of construction waste in major Chinese urban agglomeration.

The illegal dumping of construction waste (CW) poses an increasingly serious environmental pollution problem with the accelerated rate of urbanization. As CW disposal capacity struggles to match municipal needs, some CW is being diverted to higher resource endowment cities rather than recycled. To address this situation, it is necessary to obtain reliable information on the characteristics and evolution of CW generation networks in China. This study combines a modified gravity model with Social Network Analysis (SNA) to analyze the spatial association networks of CW generation in four Chinese urban agglomerations between 2000 and 2020. Results reveal the evolution characteristics of the CW generation network, including increasing density and correlation and decreasing network efficiency. Furthermore, the Quality Assurance Procedure (QAP) indicates that urbanization level and population size are positively correlated with CW generations, whereas distance plays a negative role, but resources are insignificant for network formation. The findings provide insight into current patterns of waste distribution and a theoretical basis for government policy formulation in the future.

Simulation analysis of an adjusted gravity model for hospital admissions robust to incomplete data.

BACKGROUND: Gravity models are often hard to apply in practice due to their data-hungry nature. Standard implementations of gravity models require that data on each variable is available for each supply node. Since these model types are often applied in a competitive context, data availability of specific variables is commonly limited to a subset of supply nodes. METHODS: This paper introduces a methodology that accommodates the use of variables for which data availability is incomplete, developed for a health care context, but more broadly applicable. The study uses simulated data to evaluate the performance of the proposed methodology in comparison with a conventional approach of dropping variables from the model. RESULTS: It is shown that the proposed methodology is able to improve overall model accuracy compared to dropping variables from the model, and that model accuracy is considerably improved within the subset of supply nodes for which data is available, even when that availability is sparse. CONCLUSION: The proposed methodology is a viable approach to improve the performance of gravity models in a competitive health care context, where data availability is limited, and especially where a the supply nodes with complete data are most relevant for the practitioner.

How does the communication infrastructure quality of the countries along the "Belt and Road" effect the equipment export of China?

Under the influence of COVID-19 in the past few years, the world has been facing tremendous changes. The world economy is also facing strong headwinds on its path toward recovery, and global development has suffered major setbacks. Under this background, China's equipment export has also been greatly affected. In order to maintain a stable export of equipment manufacturing to the "Belt and Road" countries, this article constructs an extended gravity model with communication infrastructure variables using the statistical data of China's equipment manufacturing exports from 2010 to 2020 and discusses the impact of "Belt and Road" countries' communication infrastructure quality on China's equipment manufacturing exports by product and country heterogeneity analysis. The results indicate significant differences in the quality of communication infrastructure among countries along the "Belt and Road". Overall, the improvement of "Belt and Road" countries' communication infrastructure quality can promote the export of China's equipment manufacturing industry. Except for the electrical machinery and office instrument, the quality improvement of "Belt and Road" countries' communication infrastructure has a positive effect on other subdivision products of China's equipment manufacturing industry, but the intensity of the effect is heterogeneous. The improvement of transition economies' communication infrastructure quality significantly promotes the export of China's equipment manufacturing industry, but the developed and developing economies' promotion effect is not significant.

Spatial Patterns and Determinants of Inter-county Migration in California: A Multilevel Gravity Model Approach.

Understanding migration patterns and their determinants is crucial for population estimation and resource allocation for policymakers. Utilizing residential mobility data collected by the Department of Motor Vehicles, this present study provides a spatiotemporal analysis of inter-county migration in California for the period 2014-2021. We use multilevel gravity models to address the hierarchical nature of migration data and the effects of migration flows sharing common origins, destinations, and regions, providing a substantively complete examination of push and pull forces affecting migration. Our findings show that populous counties in Southern California and the San Francisco Bay Area represent the largest origins and destinations, despite a systemic decline in intra-state migration. Migration is strongly associated with population size, geographic proximity (i.e., distance and contiguity), job availability, and industrial composition similarity between origins and destinations. Our findings also highlight the contribution of shared origins, destinations, and regions in explaining the systematic variation of migration flows. Counties vary more in the number of migrants they attract than the number they send. The purposed multilevel modeling approach is useful in identifying place-specific influences on migration and in improving estimation accuracy. Supplementary Information: The online version contains supplementary material available at 10.1007/s11113-023-09782-2.

EXITitis in the UK: Gravity Estimates in the Aftermath of Brexit.

The withdrawal of the United Kingdom from the European Union has had disruptive effects on international trade. As part of its 'Global Britain' strategy in the wake of Brexit, the UK is pursuing a series of Free Trade Agreements with countries around the world, including Canada, Japan, Korea, Mexico, Norway, Switzerland, Turkey and possibly the United States. Closer to home, the UK is under mounting pressure to dissuade Scotland, Northern Ireland and Wales from seeking independence to regain the severed ties with the EU. We analyze the economic consequences of these scenarios with a state-of-the-art structural gravity model for major economies around the world. We find that 'Global Britain' yields insufficient trade creation to compensate for Brexit-induced trade losses. Our results also reveal that secession from the UK in itself would inflict greater post-Brexit economic harm on the devolved nations of Great Britain. Nevertheless, these effects could be offset when secession from the UK is combined with regained EU membership.

Does Distance Still Matter? Moderating Effects of Distance Measures on the Relationship Between Pandemic Severity and Bilateral Tourism Demand.

This study aims to investigate the moderating effects of various distance measures on the relationship between relative pandemic severity and bilateral tourism demand. After confirming its validity using actual hotel and air demand measures, we leveraged data from Google Destination Insights to understand daily bilateral tourism demand between 148 origin countries and 109 destination countries. Specifically, we estimated a series of fixed-effects panel data gravity models based on the year-over-year change in daily demand. Results show that a 10% increase in seven-day smoothed COVID-19 cases led to a 0.0658% decline in year-over-year demand change. The moderating distance measures include geographic, cultural, economic, social, and political distance. Results show that long-haul tourism demand was less affected by a destination's pandemic severity relative to tourists' place of origin. The moderating effect of national cultural dimensions indulgence versus constraints was also confirmed. Lastly, a discussion and implications for international destination marketing are provided.

Optimization of landscape pattern in the main river basin of Liao River in China based on ecological network.

As a main stream method of landscape pattern optimization, the ecological network plays an important role in maintaining ecosystem stability, improving landscape connectivity, and promoting landscape sustainable development. Based on landscape connectivity index and morphological spatial pattern analysis (MSPA), a comprehensive evaluation system of ecological patches was constructed in the main river basin of Liao River, and ecological sources were extracted. According to the habitat characteristics of the study area, the ecological cumulative resistance surface was constructed, and the ecological corridors and nodes were extracted by the minimum cumulative resistance (MCR) model. The ecological network of the study area was comprehensively evaluated by using the network analysis method, and the importance level of the ecological corridor was divided by the gravity model, so as to put forward the optimization suggestions of the landscape pattern based on the ecological network. The results showed that the ecological network in the main river basin of Liao River is composed of 20 ecological sources, 108 ecological corridors, and 72 ecological nodes, with the distribution characteristics of dense east and sparse west. The main landscape components are cropland and woodland. The closure degree, line point rate, and connectivity index of the ecological network are 0.27, 1.50, and 0.51, respectively, and the cost ratio is 0.23. In the optimization of landscape pattern, priority should be given to the restoration of primary ecological sources and ecological corridors, followed by the ecological construction of secondary and tertiary ecological sources and ecological corridors, the rational use of engineering technology for habitat remodeling, and the adoption of the "patch-corridor-substrate" model to improve the stability and landscape connectivity of the regional ecosystem. The construction of ecological network in the main river basin of Liao River is of great significance to regional ecological security and biodiversity conservation, and provides data support for optimizing the landscape pattern of the basin and promoting regional sustainable development.

Improving the ecological network optimization with landscape connectivity: a case study of Neijiang City, Sichuan Province.

Rapid urbanization intensifies the fragmentation of landscape patches and affects the stability of ecosystems. The construction of an ecological network can effectively promote the connection of important ecological spaces and improve the landscape integrity. However, the landscape connectivity, directly affecting the stability of ecological network, was less considered in the ecological network construction of recent researches, which easily caused the instability of constructed ecological network. Therefore, this study introduced landscape connectivity index to establish a modified ecological network optimization method based on the minimum cumulative resistance (MCR) model. The results showed that, compared with the traditional model, the modified model focused on the spatially detailed measurement of regional connectivity, and emphasized the impact of human disturbance on ecosystem stability at the landscape scale. The constructed corridors in the optimized ecological network of the modified model not only effectively improved the connection degree between important ecological sources but also avoided the areas with low landscape connectivity and high obstacles to ecological flow, especially in the counties of Zizhong, Dongxing, and Longchang within the focal study area. The ecological network established by the traditional model and modified model generated 19 and 20 ecological corridors with lengths of 334.49 km and 364.35 km, respectively, and the number of ecological nodes was 18 and 22. Evaluated by the Gravity method, the modified model identified the important ecological corridors in the ecological network, and the energy transfer efficiency of the network was improved. This study provided an effective way to improve the structural stability of ecological network construction and can provide scientific support for regional landscape pattern optimization and ecological security construction.