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"industry structure"
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Salt production and social hierarchy in ancient China : an archaeological investigation of specialization in China's Three Gorges
\"This book examines the organization of specialized salt production at Zhongba, one of the most important prehistoric sites in the Three Gorges of China's Yangzi River valley\"-- Provided by publisher.
Book
How does the location of high-speed railway stations affect the emission reduction effect of industrial structure upgrading?
The opening of high-speed railway (HSR) has accelerated the reorganization and reallocation of regional production elements, and constantly promoted the adjustment and upgrading of industrial structure. The cleaner production effect produced by industrial structure upgrading is of great significance to industrial pollution reduction. As a bridge connecting resources within the city and elements outside the city, the location of HSR stations has become an important factor affecting the structural emission reduction effect. Based on the data of 285 prefecture level and above cities in China from 2004 to 2018, this paper investigates the structural emission reduction effects of HSR opening and the impact of HSR station location on it by employing difference-in-differences (DID) model combined with mediation effect method. The results demonstrate that the opening of HSR has significant structural emission reduction effect, and the upgrading of inter-industry structure and the intra-industry structure are important mechanisms for HSR to achieve industrial emission reduction. The structural emission reduction effect of HSR opening is closely related to the location of HSR stations. With the increase of the distance between HSR station and city center, the industrial structure upgrading effect will continue to weaken, thus inhibiting the exertion of structural emission reduction effect, of which 10 km away from the city center is the optimal site strategy for the HSR service to give full play to the structural emission reduction effect, and exceeding 50 km will be significantly detrimental to its role in promoting industrial structure upgrading.
Journal Article
Night train to Odessa : covering the human cost of Russia's war
When Russian tanks rolled into Ukraine, millions of lives changed in an instant. Millions of people were suddenly on the move. In this great flow of people was a reporter from the north of Scotland. Jen Stout left Moscow abruptly, ending up on a border post in southeast Romania, from where she began to cover the human cost of Russian aggression. Her first-hand, vivid reporting brought the war home to readers in Scotland as she reported from front lines and cities across Ukraine. Stories from the night trains, birthday parties, military hospitals and bunkers: stories from the ground, from a writer with a deep sense of empathy, always seeking to understand the bigger picture, the big questions of identity, history, hopes and fears in this war in Europe.
BOOK
Predation risk, market power and cash policy
PurposeThe purpose of the present study is to discuss the combined effect of predation risk and firms' market power on cash holdings.Design/methodology/approachThe authors tested hypotheses by using consolidated financial data in Japanese firms.FindingsThe authors find that firms' cash holdings increase with a rise in predation risk faced by firms. However, the higher the firm's market power, the weaker the above interplay becomes. Moreover, the authors find that even when firms' investments are decreased at the industry level, firms with larger cash holdings seek to mitigate predation risk by funding strategic investments with the potential to steal rivals' market share.Originality/valueThe authors recognize the importance of a firm's market power. Take a firm's market power into consideration to analyze the mechanism of a firm's cash holdings, there is a possibility that the mechanism of a firm's cash holdings as presented by the previous studies will be changed.
Journal Article
Empirical Analysis of Financial Agglomeration, Industrial Infrastructure, and Economic Growth of the Marine Industry: Using the PVAR Model to Analyze China's Marine Economy
Du, J.; Yan, B., and Su, X., 2025. Empirical analysis of financial agglomeration, industrial infrastructure, and economic growth of the marine industry: Using the PVAR model to analyze China's marine economy. Journal of Coastal Research, 41(1), 122–130. Charlotte (North Carolina), ISSN 0749-0208. To promote the high-quality development of China's marine economy, based on the panel data of 11 coastal provinces and cities from 2006 to 2016, the panel vector autoregression (PVAR) model was used to analyze the dynamic relationships among financial agglomeration, upgrades in marine industrial structure, and economic development. Based on the cointegration test, a long-term stable relationship was found between the three variables of financial agglomeration, marine industry structure upgrades, and the development of the marine economy, where financial agglomeration inhibits the growth of the marine economy, and a suitable interaction mechanism cannot be formed between the two, whereas upgrading of the marine industry structure is conducive to the development of the marine economy. In the short term, financial agglomeration can promote the upgrading of the marine industry structure, but the long-term effect is not ideal; in the long run, the development of the marine economy can encourage improvement in the level of financial agglomeration. In the short term, the development of the marine economy is mainly affected by itself and the upgraded marine industry structure, but in the long run, the inhibitory effect of financial agglomeration to the marine economy is greater.
Journal Article
The Evolution of Gender Gaps in Industrialized Countries
Women in developed economies have made major advancements in labor markets throughout the past century, but remaining gender differences in pay and employment seem remarkably persistent. This article documents long-run trends in female employment, working hours, and relative wages for a wide cross section of developed economies. It reviews existing work on the factors driving gender convergence, and novel perspectives on remaining gender gaps. Finally, the article emphasizes the interplay between gender trends and the evolution of the industry structure. Based on a shift-share decomposition, it shows that the growth in the service share can explain at least half of the overall variation in female hours, both over time and across countries.
Journal Article
Driving Factors of CO2 Emissions: Further Study Based on Machine Learning
Greenhouse gases, especially carbon dioxide (CO 2 ) emissions, are viewed as one of the core causes of climate change, and it has become one of the most important environmental problems in the world. This paper attempts to investigate the relation between CO 2 emissions and economic growth, industry structure, urbanization, research and development (R&D) investment, actual use of foreign capital, and growth rate of energy consumption in China between 2000 and 2018. This study is important for China as it has pledged to peak its carbon dioxide emissions (CO 2 ) by 2030 and achieve carbon neutrality by 2060. We apply a suite of machine learning algorithms on the training set of data, 2000–2015, and predict the levels of CO 2 emissions for the testing set, 2016–2018. Employing rmse for model selection, results show that the nonlinear model of k-nearest neighbors (KNN) model performs the best among linear models, nonlinear models, ensemble models, and artificial neural networks for the present dataset. Using KNN model, sensitivity analysis of CO 2 emissions around its centroid position was conducted. The findings indicate that not all provinces should develop its industrialization. Some provinces should stay at relatively mild industrialization stage while selected others should develop theirs as quickly as possible. It is because CO 2 emissions will eventually decrease after saturation point. In terms of urbanization, there is an optimal range for a province. At the optimal range, the CO 2 emissions would be at a minimum, and it is likely a result of technological innovation in energy usage and efficiency. Moreover, China should increase its R&D investment intensity from the present level as it will decrease CO 2 emissions. If R&D reinvestment is associated with actual use of foreign capital, policy makers should prioritize the use of foreign capital for R&D investment on green technology. Last, economic growth requires consuming energy. However, policy makers must refrain from consuming energy beyond a certain optimal growth rate. The above findings provide a guide to policy makers to achieve dual-carbon strategy while sustaining economic development.
Journal Article
Structural configuration of sustainable sports industry based on deep learning and genetic algorithm
To address the structural imbalances and sustainability challenges faced by the sports industry in its high-quality development transformation, and to overcome the shortcomings of existing research in dynamic modeling, multi-objective optimization, and quantitative solutions, this paper constructs an intelligent optimization framework integrating deep learning (DL) and genetic algorithms (GA). This framework uses a one-dimensional convolutional neural network to extract deep features and predict trends from high-dimensional industry data, and employs a genetic algorithm as the optimization engine to find the Pareto optimal solution that synergistically improves economic, social, and environmental benefits. Based on industry statistics from 2010 to 2022 and provincial panel data from 2018 to 2020, the study reveals the core characteristics of the sports industry’s transformation towards service orientation and verifies the impact of the pandemic, which resulted in a 7.2% and 4.6% decrease in total industry output and added value in 2020 compared to 2019, respectively. After adaptation and validation with data from the United States and Germany, the model’s cross-regional prediction error is less than 6%, and the innovation-driven path can increase the proportion of sports services to 68.5% by 2025. This paper breaks through the traditional static description paradigm and provides an intelligent decision-making tool that combines theoretical depth and practical value, offering a new paradigm and empirical support for the precise optimization of industrial structure and cross-regional application.
Journal Article
The optimization path of agricultural industry structure and intelligent transformation by deep learning
This study addresses key challenges in optimizing agricultural industry structures and facilitating intelligent transformation through the application of deep learning algorithms and advanced optimization techniques. An intelligent system for agricultural industry optimization is developed, with convolutional neural networks, recurrent neural networks, Long Short-Term Memory networks, and generative adversarial networks introduced for tasks such as image recognition, time series forecasting, and synthetic data generation. Subsequently, a hybrid optimization method is designed, combining the Genetic Algorithms with particle swarm optimization to improve the model’s global search capability and local convergence speed. The performance of these techniques is rigorously evaluated through extensive experimentation. The results demonstrate that the proposed method outperforms conventional algorithms in regression tasks, particularly in terms of computational efficiency, data processing speed, and model training stability, while also exhibiting high scalability. In crop yield prediction, the proposed method achieves superior performance, as evidenced by reductions in both absolute error and mean squared error, along with attaining the highest R
2
value (0.93). Additionally, in pest and disease detection, the proposed method exceeds other models in accuracy (97.5%), precision (96.8%), recall (97.2%), and F1 score (0.97), underscoring its superior performance in detecting agricultural pests and diseases. The method also significantly surpasses traditional algorithms in crop disease identification accuracy, climate change prediction precision, and the quality of synthetic data generation. This study offers novel technical solutions and decision-making tools for advancing intelligent agriculture.
Journal Article
Collaborative Promotion of Technology Standards and the Impact on Innovation, Industry Structure, and Organizational Capabilities: Evidence from Modern Patent Pools
This study explores the impact of modern patent pools—inter-organizational collaborative arrangements for promoting the adoption of technology standards—on the rate of follow-on innovations based on pooled technologies, the vertical structure of associated industries, and organizational capabilities of noncollaborating firms. On one hand, the formation of modern pools can boost follow-on innovation by lowering the search, negotiation, and licensing costs associated with pooled standards. On the other hand, modern pools may decrease the incentives to invest in follow-on innovations because of cannibalization risks and grant-back provisions. To the extent that modern pools succeed in establishing a dominant standard, their collaborative nature and their reliance on markets for technology can reduce technological uncertainty and appropriation hazards, hence triggering vertical disintegration in related industries. Moreover, by establishing a dominant standard, modern pools can effectively diminish the relative importance of integrative capabilities inside firms. Employing a combination of empirical strategies, I show that the formation of seven major modern patent pools has, on average, increased the rate of follow-on innovations based on the pooled standards by about 14%. Moreover, the results suggest that the establishment of modern pools can facilitate a shift toward vertical disintegration in associated industries where upstream technology-focused organizations would disproportionally contribute to the development of follow-on complementary technologies. The results also suggest that modern pools reduce the relative importance of integrative capabilities and provide a more advantageous position for specialized startups vis-à-vis diversifying entrants. I discuss the implications for literatures on organizational economics, organizational capabilities, business ecosystems, standards, and nascent industries.
Journal Article