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Urban underground space (UUS) development has been acknowledged as a positive contribution to urban resilience (UR). Such contribution has been qualitatively addressed in recent years, but only quantitatively discussed in few studies. Quantitative evaluation methods for UR are widely used in China and around the world, but the role of underground space is barely included. This paper provides a way to bridge this gap on the city scale. A UR evaluation framework was carefully constructed that covers the basic aspects and elements of UR. The contributions of UUS to UR were identified and integrated into the UR evaluation framework, and the measurement methods for each indicator related to UUS were determined. A case study of 19 sample cities in China were conducted using the integrated evaluation model. Correlation analysis and clustering analysis were further adopted to interpret the evaluation results, mainly with three indicators reflecting the level of UUS development, namely UUS area (m²), UUS density (104 m² /km² ) and UUS area per capita (m² / person). The results showed a strong correlation between UUS area and UR. The average proportion of UR provided by UUS in the 19 sample cities was 16.46%, while the maximum figure reached 29.20%. The sample cities were clustered into four categories based on the relationship between the proportion of UR provided by UUS, UUS area, and GDP per capita, where both high and low UUS area tend to provide less proportion of resilience than the medium UUS area. Corresponding suggestions for UUS utilization were proposed to assist cities in achieving urban resilience. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Ilmu Ekonomi Terapan is the property of Universitas Airlangga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper asks the following question: can the macroeconomic fluctuations of Gross Domestic Product (GDP) explain the electoral success of political parties? Notwithstanding other important determinants such as the charisma of the leader of a popular political party or ideology, the economy may very well explain electoral successes. In this paper, I use a very simple variable to account for the economy: the rate of growth of GDP. It is claimed that years of relative higher growth are associated with the incumbent government retaining its power, while years of zero or negative growth rates are related with the fall of an incumbent government. The period between 1974-2023 shows evidence of Greek economic growth and the impact on elections and therefore the hypothesis cannot be rejected that the economy plays a central role in explaining electoral successes, and especially failures, of incumbent governments. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to show how the self-exciting threshold autoregressive (SETAR) model might be a suitable econometric framework for characterizing the dynamics of the US public debt/GDP ratio after the Bretton Woods collapse. Our preferred SETAR specifications are capable of capturing the main stylized facts of the US public debt/GDP ratio between 1974 and 2024. In addition, the estimated SETAR models are consistent with theoretical frameworks that look to explain the behavior of the US public debt/GDP ratio before and after the Global Financial Crisis (GFC). Finally, under the assumption of public debt/GDP ratio stationarity, for which we find only limited and inconclusive evidence, this paper provides some arguments for why previous studies, which use the exponential smooth threshold autoregressive (ESTAR) models, logistic smooth threshold autoregressive (LSTAR) models or SETAR-type models for the first differences of the US public debt/GDP ratio, are potentially misspecified on both econometric and economic grounds. [ABSTRACT FROM AUTHOR]

This paper examines the nexus between carbon dioxide (CO2) emissions, electricity consumption, fossil fuels, foreign direct investment (FDI), gross domestic product (GDP), and renewable energy in the Philippines. This paper also explores the intricate relationships between carbon dioxide (CO2) emissions, electricity consumption, fossil fuel use, foreign direct investment (FDI), gross domestic product (GDP), and renewable energy in the Philippines. Utilizing time-series data from 1990 to 2022 and applying advanced econometric techniques such as vector error correction modeling (VECM) and Granger causality tests, the study reveals the significant impacts of economic growth and energy consumption on CO2 emissions. The findings highlight the crucial role of renewable energy in mitigating environmental degradation. Policy implications are discussed in the context of the Philippines' commitment to sustainable development and climate change mitigation, emphasizing the need for integrated policies that promote renewable energy and energy efficiency alongside economic growth. We use a comprehensive econometric analysis to understand these variables' dynamic interactions and causal relationships. The study employs time-series data from 1990 to 2022 and applies advanced econometric techniques, including vector error correction modeling (VECM) and Granger causality tests. The results highlight the significant impact of economic growth and energy consumption on CO2 emissions while also underscoring the critical role of renewable energy in mitigating environmental degradation. Policy implications are discussed considering the Philippines' commitment to sustainable development and climate change mitigation. [ABSTRACT FROM AUTHOR]

This paper tests the hypothesis that action-based fiscal consolidations have a negative effect on GDP growth. Using the IMF's dataset on action-based fiscal consolidations, instrumental variables' regressions show that action-based fiscal consolidations have a significant positive effect on GDP growth. The instrumental variables' regressions also show that action-based fiscal consolidations significantly increase investment and productivity. The findings presented in this paper thus strongly reject the hypothesis that action-based fiscal consolidations reduce growth. The paper argues that least squares estimates presented in previous literature suffer from negative reverse causality bias: GDP growth has a significant positive effect on both the likelihood and the magnitude of action-based fiscal consolidations. To uncover causal effects of action-based fiscal consolidations, researchers need to use an instrumental variables approach. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the Polish Association of Agricultural & Agribusiness Economists is the property of Polish Association of Agricultural & Agribusiness Economists and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Agriculture was and remains the support of human existence, representing the backbone of the Romanian rural economy. The gross domestic product represents a macroeconomic indicator of results, being the consequence of the efficient use of resources. In this context, the authors analyze the place that agriculture holds in achieving the gross domestic product in Romania compared to the European Union, as well as its dynamics. In this sense, the share of resources in the formation of the gross domestic product in the period 2000-2022 is presented. The main research methods used for this work are: analysis, synthesis, comparison and mathematical analysis. From the analysis carried out, conclusive ideas emerge, and the article becomes even more suggestive, in that it is accompanied by tables synthesized by the authors and relevant graphics that highlight and support the assessments that the authors make in the content of this article. At the end of the paper, the authors emphasize the fact that the GDP is the most complex indicator that reflects the transformations that the Romanian economy has gone through in recent years. From the point of view of the use of resources, it can be seen that the share of the agricultural sector has registered a downward evolution, which can be largely explained by the increase in the importance of industry and the service sector in the overall national economy. [ABSTRACT FROM AUTHOR]

China is transitioning its industrial structure from labor-and resource-intensive industries that previously contributed significantly to the country's GDP growth to technology-intensive industries emphasizing a highly-skilled workforce and sustainability to achieve high-quality economic growth. This paper examines the impact of the RMB exchange rate on high-quality economic growth through theoretical modeling and empirical analysis and discusses the variable of overseas education to explore the mechanism of how the RMB exchange rate and overseas education jointly impact high-quality economic growth. The research sample includes the National Bureau of Statistics data on education from 1995 to 2015, the Bank for International Settlements (BIS) data on the RMB exchange rate, and the added value of China's high-quality economic growth estimated based on the national economy data. An empirical analysis of theoretical expectations was conducted, finding that RMB appreciation could make a positive contribution to China's high-quality economic growth; RMB exchange rate fluctuations would impact the relative cost of overseas education and overseas returnees could have a positive impact on domestic resource utilization efficiency and domestic capacity to make sci-tech innovations, thereby injecting vitality to high-quality economic growth. This study focuses on both the RMB exchange rate and the population studying abroad, providing additional observation dimensions to existing research. [ABSTRACT FROM AUTHOR]

Micro and large-sized enterprises are important elements to enhance the economic growth of any country, and even more so for developing countries such as Mexico. These enterprises highly contribute to job generation, competitiveness, and gross domestic product, factors that are important for the developing of a nation. The aim of this paper is to study the impact of human capital investments in the efficiency of the 21 economic activity subsectors for micro and large-sized enterprises in the Mexican manufacturing industry between 2009–2021. The database come from Mexico Annual Manufacturing Industry Survey. Four Data Envelopment Analysis models are developed to study the relationship between annual average working days, annual average wages, and annual average investment in training with average sales per year. Data indicate that, most of the micro-sized enterprises of the Mexican manufacturing sector do not invest in human capital training, contrary to their large-sized enterprises. The results show that investing in human capital training increase sales and wages in micro-sized enterprises of the Mexican manufacturing industry, but it is not evident in large-size enterprises of the Mexican manufacturing industry. The calculation of the economic activity subsectors efficiencies using the developed Data Envelopment Analysis models indicate that all the economic activity subsectors with scale efficiency equal to one optimally invest, and the average amount of investments in human capital training needed to increase the global and pure technical efficiencies of the others are calculated with the developed Data Envelopment Analysis models. In the three main economic activity subsectors of the Mexican manufacturing industry, a significant increase—in 83.33% of cases—in wages and salaries is seen in both micro and large-sized enterprises. Particularly, the results indicate that the Chemical industry economic activity subsectors show the highest efficiency in both micro and large-sized enterprises when the human capital training variable is present. This paper demonstrates the importance of investing in human capital to enhance the efficiency of micro and large-sized enterprises. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Ilmu Ekonomi Terapan is the property of Universitas Airlangga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Gross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy's health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To enhance GDP prediction performance, we implement a parameter transfer approach, fine-tuning the parameters learned from Dataset A on Dataset B. Moreover, in this study, a preprocessing stage that includes median imputation and data normalization is performed. Mean Square Error, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Median Absolute Error, and determination coefficient (R2) evaluation metrics are utilized in this study to demonstrate the performance of the proposed model. The experimental results demonstrated that the proposed model gave better results than other regression models used in this study. Also, the results show that the proposed model achieved the highest results for R2, with 99.99%. This paper addresses a critical research gap in the domain of GDP prediction through artificial intelligence (AI) algorithms. While acknowledging the widespread application of such algorithms in forecasting GDP, the proposed model introduces distinctive advantages over existing approaches. Using PC-LSTM-RNN which achieves high R2 with minimum error rates. [ABSTRACT FROM AUTHOR]

In this study, the role and importance of agriculture-based investments in sustainable development were investigated. For this purpose, a situation analysis of these matters was conducted for Yozgat province, which is from the 1st Degree Priority Regions in development. In the study, interviews were conducted with managers or expert personnel of institutions and organizations related to the subject. Data obtained via the interview were the primary data of the study. Papers and books published related to the subject, and data obtained from the database of the Ministry of Agriculture and Forestry (MAF) and the Turkish Statistical Institute (TSI) were also the secondary data of this study. According to the research findings, it is seen that significant investments based on agriculture have been made in the province. Among these investments, there are sugar factory (2 units), Kabalı Integrated Orchard (Turkey's largest integrated orchard), geothermal greenhouses, flour production (12 units), milk and milk products (12 units), meat and meat products (11 units). and fruit and vegetable processing-packaging facilities (4 units). With investment depended on agriculture, there have been positive developments in per capita gross domestic product, employment, labor force participation rate and unemployment rate in the province. While in 2004, gross domestic product per capita was USD 3,272, employment rate was 31.6%, the participation rate to labor force was 36.2% and unemployment rate was 12.8%, in 2021 these were, respectively, USD 4,869, 48.5%, 55.1% and 12%. In Kabalı Integrated Orchard, which is one of the largest orchards in Turkey, in addition to orcharding, there have been animal breeding activities such as goose, sheep and beekeeping. The cherry orchard located in the integrated facility is the third-largest cherry orchard in Europe. This study is important in terms of the sustainability of investments depending on agriculture and the formation of strong expectations for rural development. [ABSTRACT FROM AUTHOR]

Existing macroeconomic forecasting methods primarily focus on the characteristics of economic data, but they overlook the energy-related features concealed behind these economic characteristics, which may lead to inaccurate GDP predictions. Therefore, this paper meticulously analyzes the relationship between energy big data and economic data indicators, explores the coupling feature mining of energy big data and economic data, and constructs features coupling economic and energy data. Targeting the nonlinear variation coupling features in China's quarterly GDP data and using the long short-term memory (LSTM) neural network model based on deep learning, we employ wavelet analysis technology (WA) to decompose selected macroeconomic variables and construct a prediction model combining LSTM and WA, which is further compared with multiple benchmark models. The research findings show that, in terms of quarterly GDP data prediction, the combined deep learning model and wavelet analysis significantly outperform other methods. When processing structurally complex, nonlinear, and multi-variable data, the LSTM and WA combined prediction model demonstrate better generalization capabilities, with its prediction accuracy generally surpassing other benchmark models. [ABSTRACT FROM AUTHOR]

The automotive industry has always represented a key sector within the economy of any country. Moreover, its significant contribution to the gross domestic product of many countries transformed this industry into one of the main pillars of the global economy. Therefore, numerous countries have built and produced various motor vehicles on different continents. The purpose of the paper is to briefly present and analyze the evolution of the world motor vehicle production on the four continents (Europe, America, Asia-Oceania, and Africa) in the period 2018-2022. The research methodology is based on a qualitative research method. The paper shows that the pre-pandemic period was a better period for the automotive industry than the pandemic period. Also, it demonstrates that Asia- Oceania dominates the production hierarchy by continents. [ABSTRACT FROM AUTHOR]

The present paper presents a statistical analysis of the Gross Domestic Product/person, at the national level, in relation to the member countries of the European Union, using statistical data published by Eurostat, Naational Institute of Statistics (INSSE), FAOSTAT and the World Bank. Based on the data centralization, calculations were made to determine some absolute and relative statistical indicators, such as the absolute change, the dynamic index or the dynamic rate. The purpose of this paper is to analyze and observe GDP per inhabitant evolution, in the period 2017 - 2022, as well as the hierarchy of European member countries, according to this macroeconomic indicator. Going through the work, it can be observed that the order of each member state in different hierarchies based on the GDP level, nominal or real, differs, depending on the perspective from which the data is analyzed. Not always a country with a high level of GDP is characterized by a higher level of well-being of the population. Also, a country that has not yet reached economic maturity in terms of GDP level, may have advantages over the already mature European economic markets over a longer period of time. [ABSTRACT FROM AUTHOR]

In the current context, after a pandemic that has left traces in all socio-economic fields and during major geopolitical problems, Basic research is needed in order to identify directions for rapid and sustainable socio-economic recovery. The agricultural sector has played a particularly important role in speeding economic growth and development in less developed countries. As a result, we may argue that the agriculture sector has aided these countries' integration into global trade. The proposed paper aims to identify the degree of dependence of the Moldovan economy on agriculture and how this dependence can be transformed into a challenge that will help the country's economy. Also, the paper aims to identify the link between agricultural production and gross domestic product at the level of the Moldovan economy, as well as the need to create a stability in the labour market in agriculture. The above mentioned will be achieved in the conditions in which there is already a decrease in the labour force, a decrease due to medical, geo-political and migration factors. To develop a sustainable economy, sustainable agricultural public policies must be enacted, resulting in the attraction of personnel from metropolitan regions (where unemployment is higher) to rural areas. [ABSTRACT FROM AUTHOR]

Background We previously showed that the number of publications in dermatology is increasing year by year, and positively correlates with improved economic conditions in mainland China, a still developing Asian country. However, the characteristics of publications in dermatology departments in more developed Asian countries such as Japan and South Korea are unknown. Methods In the present study, publications from 2003 through 2012 in dermatology in Japan, South Korea and mainland China were characterized. All data were obtained from www.pubmed.com. Results Dermatology departments in Japan published 4,094 papers, while mainland China and South Korea published 1528 and 1,758 articles, respectively. 48% of articles from dermatology in Japan were original research and 36% were case reports; The number of publications in Japan remained stable over time, but the overall impact factors per paper increased linearly over the last 10 year period (p < 0.05). In mainland China, 67% of articles from dermatology were original research, while 19% were case reports; The number of publications and their impact factors per paper increased markedly. In South Korea, 65% of articles from dermatology were original research and 20% were case reports. The impact factors per paper remained unchanged, despite of the fact that the number of publications increased over the last 10 year period (r2 = 0.6820, p = 0.0032). Only mainland China showed a positive correlation of the number of publications with gross domestic product per capita during this study period. Conclusions These results suggest that the total number of publications in dermatology correlates with economic conditions only in developing country, but not in more developed countries in Asia. The extent of economic development could determine both the publication quantity and quality. [ABSTRACT FROM AUTHOR]

The article focuses on the payment systems of several developed countries using payments data for the years 1987 to 1993. It states that cash is the most used instrument of payment. It mentions that surveys on the subject have the proportion of cash transactions among all transactions at 90 percent for Great Britain, 86 percent for Germany, 83 percent for the U.S. and Finland, and 78 percent for the Netherlands. It states that an alternative measure of cash use is the ratio of currency value to gross domestic product.