Investment In Fixed Assets Research Articles

Prediction of China's industrial carbon peak: Based on GDIM-MC model and LSTM-NN model

The industrial sector is the key area for China to achieve the carbon peaking goals, as it accounts for more than 65 % and 70 % of the national total energy consumption and carbon emissions. However, the discussion on the time and route of carbon peak in China in the existing literature is still quite different. In this study, we establish three scenarios and comprehensively used Monte Carlo simulation and LSTM Neural Network model to predict the evolution trends of China's industrial carbon emissions during 2020–2030. Firstly, the decomposition results of the Generalized Divisia Index Method shows that fixed assets investment is the most important factor for promoting and carbon intensity of investment is the key for reducing carbon emissions. Then, basing on the Monte Carlo dynamic simulation, we could draw the three kinds of carbon emissions route that it will peak in 2031 in the Baseline scenario, in the Green Development scenario (environmental policy improvement) and Technological Breakthrough scenario (green technology progress) will peak in 2027 and 2025, and under the LSTM Neural Network model, peak time will occur in 2028. Comparing the results of above predictions, China's industrial carbon emissions could peak by 2030(in GD scenario, 2027; TB scenario, 2025). Finally, we discuss the path of China's industrial carbon emissions reduction and provide a reference for the rational formulation of low-carbon regulatory policies in the future and the realization of sustainable development.

Enhanced management information disclosure responsibilities and corporate risk-taking: Evidence from the accountability system for errors in China

Enhancing management's responsibility for information disclosure can improve a firm's risk-taking by improving its resource allocation efficiency, but it can also reduce firm risk-taking by increasing managers' risk aversion. After the regulation change from the China Securities Regulatory Commission to require the listed companies to establish an accountability system for material errors in information disclosure of annual reports (hereinafter referred to as the “accountability system for errors”) in late 2009, the risk of misreporting became substantially higher. This paper explores how the accountability system for errors with strengthened management's responsibility for information disclosure affects corporate risk-taking. We find that the accountability system for errors significantly reduces corporate risk-taking, especially idiosyncratic risk, and the impact is greater in firms with short-term CEO tenure and high-tech firms. The improved information environment and increased market pressure are the mechanisms by which the accountability system for errors reduces corporate risk-taking. Further research shows that after the policy change, firms reduce R&D expenditures and output and are more likely to diversify risk through fixed asset investment and diversified mergers and acquisitions. After establishing the accountability system for errors, the higher level of risk-taking will lead to lower CEO compensation and a higher probability of CEO dismissal. This paper enriches the research on corporate governance reform related to strengthening management responsibility. Our research also provides references and guidance for relevant regulatory authorities and listed companies in emerging markets to improve their corporate governance.

Do Backbone High-Speed Rails Widen the North-South Gap in China?

The economic disparity between southern and northern China has widened in the past decade. This article explores the roles of the north-south stretched backbone high-speed rails (HSRs) in the widened north-south economic gap in China. By constructing panel data of 283 cities between 2005 and 2016 and estimating the difference in GDP and per-capita GDP of northern and southern cities before and after the first north-south stretched HSR, we show that the north-south economic gap widened by about 8% as the opening of the Beijing-Shanghai HSR, the first north-south stretched HSR. Further channel analysis reveals that the north-south gaps in population, fixed asset investment, public expenditure, and the relative size of secondary industry to tertiary industry also widened. These results suggest that fast transportation improvement caused by long-distance backbone HSRs can contribute to accelerating the large-scale regional disparity.

How do natural and socio-economic factors influence the sustainable development of the ecological environment in the World Natural Heritage Sites? Evidence from the Jiuzhaigou, China

With the rapid development of tourism in the World Natural Heritage Sites (WNHSs), it made ecological environment issues increasingly become prominent while boosting regional economic development. Exploring the influence and evolution of natural and socio-economic factors will help to maintain the authenticity and integrity of the WNHSs, and promote sustainable development. Here, to better understand the sustainable development situation of the WNHSs, we construct a nature-society-based comprehensive evaluation framework, and combining the remote sensing-based ecological index (RSEI) with the spatial autocorrelation model, and employed the obstacle factor diagnostic model, to reveal the spatial–temporal characteristics and the key driving factor of ecological environment in the Jiuzhaigou. Meanwhile, we assess the integrated ecological sensitivity of the Jiuzhaigou and determine the key conservation area. Through the analysis of results, we find that RSEI from 2013 to 2020 exhibits a fluctuation trend resembling an ”N-shape”, high RSEI points tend to cluster in the southern part of the study area, while low RSEI points cluster in the northern part, it has a significant spatial heterogeneity. We shed further light on that when the number of tourists exceeds the population carrying capacity of the heritage site, the negative impacts of tourism development on the natural environment will outweigh the positive impacts of tourism revenue growth on regional economic development and constrain the sustainable development of the site as a whole. Specifically, dissolved oxygen concentration in water, nitrogen dioxide concentration, and noise are the main drivers of nature. The proportion of tertiary industry, fixed asset investment, and total tourism consumption are the main drivers of society. Furthermore, the ecological sensitivity of Jiuzhaigou is moderately low. Therefore, we suggest that the government should improve and refine the existing disaster early warning mechanism, dynamically adjust and control the number of tourists received per day, harmonize the relationship between economic development and environmental protection, and strengthen the protection of authenticity and integrity in areas of high ecological sensitivity.

Managerial sentiment and corporate risk-taking: evidence from Chinese listed companies’ annual reports

PurposeThis paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory.Design/methodology/approachBy leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment.FindingsResults show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking.Research limitations/implicationsAnnual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures.Practical implicationsTo the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class.Originality/valueThis paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.

IPO suspension, financing uncertainty and corporate tax avoidance

The acquisition of external financing is an important factor affecting the development of enterprises and even the economic growth of a country. However, changes in the external environment often expose enterprises to uncertainties in obtaining external financing. Taking China’s initial public offering (IPO) suspension policy as a setting, this paper examines the impact of the associated external financing uncertainty on firms. The empirical results show that firms that are unable to secure planned financing due to the IPO suspension policy engage in greater tax avoidance activities than successful IPOs during the IPO suspension period; this phenomenon is mainly concentrated in firms that are not state-owned, have no venture capital or private equity backing, have lower debt servicing capacity and have lower tax avoidance risk. Moreover, the tax avoidance activities of enterprises positively influence their fixed asset investment and innovation investment during the IPO suspension period. Evidence based on IPO price performance indicates that investors respond positively to firms’ tax avoidance practices during IPO suspensions.

Does the Cultural Resource Curse Exist? Evidence from China

Utilizing cultural resources development is a popular topic. Economic development in China is expected to achieve this transformation. Based on panel data from 31 provinces in China between 2015 and 2019, this study empirically tests the impact of cultural resources on economic growth. We use a two-way fixed effects model, a moderating effect model, and a mediation effect model. The results indicate that abundant cultural resources can significantly promote overall economic growth, and this relationship still holds after robustness tests. Fixed asset investment in the cultural and tourism industries and education levels can weaken the positive impact of cultural resources on economic growth. Cultural resources can promote economic growth by increasing the number of tourists; however, some cultural resources still hinder economic growth. Regional heterogeneity exists in the impact of cultural resources on economic growth, showing an imbalance with the highest effect in the west, the second in the central area, and the lowest in the east. This study aims to promote awareness of the cultural resource curse in China.

Urban Comprehensive Carrying Capacity and Urbanization in Northeast China

The scientific evaluation and identification of the relationship between urban comprehensive carrying capacity and urbanization in Northeast China, a famous old industrial base, is an important basis for realizing the overall revitalization of the region. Using a panel data set of 34 prefecture-level cities in Northeast China from 2003 to 2019, this study constructs an ordinary panel data model to identify the relationship between urban comprehensive carrying capacity and urbanization. The results show that urbanization has significantly positive effects on urban comprehensive carrying capacity, and there is a significant inverted U-shaped curve relationship between urban comprehensive carrying capacity and comprehensive urbanization in Northeast China, especially in the shrinking cites. In addition, the economic urbanization variables of the fixed-asset investment, the total retail sales of social consumer goods, and the social urbanization variable of internet users play significantly important roles in forming of the inverted U-shaped curve relationship with the urban comprehensive carrying capacity of the shrinking cities in Northeast China. Hence, innovation-driven economic regrowth, promoting equalization of basic public services, alleviating talent outflow, and strengthening the leading roles of the core cities are effective measures for improving urban comprehensive carrying capacity and urbanization quality in Northeast China.

The Effect of Innovation Box Regimes on Investment and Employment Activity

ABSTRACT We study whether innovation box tax incentives, which reduce tax rates on innovation-related income, are associated with increased fixed asset investment and employment. Using a stacked cohort difference-in-differences design on an entropy-balanced sample of European multinationals, we find innovation box regimes are associated with higher levels of capital expenditures, relative to noninnovation box jurisdictions. We do not find discernible effects on total employment or total compensation. However, the data suggest that companies in innovation box countries have a more highly compensated workforce following innovation box implementation, particularly among patent-owning observations in countries with more restrictive innovation box regimes and greater tax benefits. Our study contributes to the literature on, and policy evaluation of, innovation box regimes by examining the extent to which these incentives result in tangible investment and employment and by identifying how different characteristics of innovation box regimes impact these outcomes.

Multi-scenario prediction and path optimization of industrial carbon unlocking in China

China's industry is facing a carbon lock-in (CLI) dilemma. Based on the complexity and dynamics of CLI, exploring diversified unlocking paths is an important way to achieve emission reduction goals. This paper innovatively applies system dynamics (SD) to the study of CLI prediction and unlocking path, and systematically constructs an industrial CLI SD model, trying to break the positive feedback mechanism of CLI through multiple causality and feedback chains. This paper simulates and predicts the changing trend of the CLI level of China's industry and 8 major subsectors from 2020 to 2050. At the same time, the random forest algorithm is used to select the regulatory variables of policy analysis. Compared with the previous research methods, the accuracy and objectivity of the selection of regulatory variables are improved, so as to test the effectiveness of the unlocking path under each scenario. The results show that (1) the whole industry faces a serious CLI problem and is still in a deep lock-in state. From the perspective of subsectors, the light, electromechanical, and textile industries have achieved unlocking; the oil and extractive industries are in a moderately lock-in state; the chemical, steel, and power industries are in a deep lock-in state; and the CLI situation is serious. (2) The results of the random forest algorithm show that R&D investment, the proportion of fixed asset investments in the energy industry, car sales, energy structure, and carbon emission industry structure are the most significant factors affecting CLI in each subsystem. (3) Compared with the existing economic development model, the technological, institutional, social, and comprehensive unlocking scenarios can all enable industry to achieve carbon peak and carbon unlocking in advance. However, the effect of each scenario is as follows: comprehensive unlocking scenario > technological unlocking scenario > institutional unlocking scenario > social unlocking scenario. (4) The unlocking times of the oil, chemical, steel, and power industries are relatively late in each scenario. To realize carbon unlocking in industry, these four areas should be focused on in the future. This study helps policymakers formulate reasonable policies to accelerate industrial carbon unlocking and promote the implementation of the “dual carbon” strategy.

Assessing the green energy development in China and its carbon reduction effect: Using a quantile approach

Developing green energy has become a critical way to deal with energy shortage and global warming. The objective is to investigate the driving factors of green energy development in China, and further evaluate the role of green energy in carbon reduction. This article uses the data-driven nonparametric model to investigate green energy development, and obtains several interesting results. (1) Technological progress has the greatest impact on the green energy development in Ningxia, Jilin, and Jiangxi provinces, due to faster R&D investment. (2) The changes in oil prices have a larger driving effect on green energy development in Heilongjiang, Liaoning, Shanxi, and Beijing. (3) Fixed asset investment contributes the most to the energy-rich provinces such as Shaanxi, Xinjiang, Inner Mongolia, and Gansu, because of faster investment in energy infrastructure. (4) The provinces with low green energy production face greater environmental pressure, owing to excessive reliance on fossil fuels. (5) Finally, the results display that the carbon reduction effect of green energy development is not prominent, on account of less green energy consumption. These empirical findings provide policy implications for China's effective promotion of green energy.

The impact of carbon emission trading policy on overcapacity of companies: Evidence from China

As a market incentive-based environmental regulation, the carbon emission trading (CET) policy is an important tool for China to achieve “carbon peak” and “carbon neutrality” goals. However, it is not clear how the CET policy will affect companies' overcapacity. Given this, based on collecting the list of the CET pilot companies, this paper uses the difference-in-differences (DID) model to explore the impact of the CET policy on companies' overcapacity. It's found that the CET policy will significantly aggravate companies' overcapacity. The results of the mechanism analysis show that the CET policy will expand companies' fixed assets investment, reduce their turnover of fixed assets, and increase the credit support and green subsidies provided by the government to companies, thereby leading to a disorderly expansion of capacity. Besides, heterogeneity analysis shows that companies' overcapacity aggravation caused by the CET policy is more prominent in regions with low marketization levels, backward economic development levels, and weak environmental regulation intensity, state-owned companies and companies with low R&D levels. This paper not only provides new research literature for the CET policy and overcapacity, but also provides more empirical evidence at the micro level for the continuous improvement of the national CET market.

A multi-dimensional analysis on potential drivers of China's city-level low-carbon economy from the perspective of spatial spillover effects

Under the background of “carbon peak” and “carbon neutrality”, China's cities are at the heart of climate change mitigation. To fulfill the commitments, the potential drivers of China's low-carbon economy at the city level should be explored. Thus, this study estimated the quality of low-carbon economy from the perspectives of carbon emissions and sequestration. Subsequently, spatial econometric approach was adopted to analyze the direct and spatial spillover effects of drivers on low-carbon economy. Moreover, to explore more potential variables, this study combined machine learning algorithms with different weight matrices to assess the importance scores of neighbouring cities' potential variables. The results show that (1) GDP per capita, emissions reduction technological progress, terrestrial vegetation's net primary productivity and the ratio of forest land cover improved cities' low-carbon economy; (2) land urbanization reduced nearby cities' low-carbon economy (especially those with high human footprint index at the inter-city border), which was caused by its negative spatial spillover effects on forest net primary production; (3) based on importance analysis, this study found that the characteristics of different sectors (e.g., the ratio of labor to fixed capital stocks, employment and fixed asset investment expenditure) also significantly influenced nearby cities' low-carbon economy.

Enterprise Spatial Agglomeration and Economic Growth in Northeast China: Policy Implications for Uneven to Sustainable Development

Many countries and regions around the world are experiencing this development dilemma, and Northeast China is a typical representative. To explore the reason for the backwardness of Northeast China, we analyze the formation mechanism and efficiency of enterprise agglomeration in this research. Based on the panel data of 34 prefectural level or above cities in Northeast China and 241 citescities in other regions from 1999 to 2015, a fixed effects panel model is used, considering enterprise agglomeration, foreign direct investment (FDI), investment in fixed assets, and the non-agricultural industry structure. It is found that enterprise agglomeration has a highly significant negative impact on urban productivity in Northeast China, and this phenomenon of agglomeration diseconomy in the northeast is most significant among the four regions of the country. Furthermore, according to the moderation mechanism analysis, the enterprise agglomeration in Northeast China reduces the pulling effects of fixed asset investment and FDI on economic efficiency but enhances the promotion effect of the non-agricultural industry structure on urban productivity. After the robustness test and extension analysis, this study determines that the enterprise agglomeration in Northeast China does not effectively stimulate the driving role of investments. Finally, we discuss measures that can help resolve the current agglomeration diseconomy problem in Northeast China and achieve sustainable development.

Impact of the Urban-Rural Income Disparity on Carbon Emission Efficiency Based on a Dual Perspective of Consumption Level and Structure

Utilizing Chinese provincial panel data from 2006–2019, this paper applies the super-efficient epsilon-based measure (EBM) model including non-desired output to measure carbon emission efficiency (CE) and analyze spatio-temporal characteristics of CE, in which social fixed asset investment, energy consumption and urban employment per unit are used as input indicators and regional GDP and CO2 emissions are used as output indicators. Additionally, we use the spatial Durbin model to explore the impact of urban-rural income disparity (URID) on carbon emission efficiency and its spatial spillover effect and explore indirect mechanisms of consumption level and consumption structure on CE using mediating effect test. The results showed that: (1) The national CE level generally declined between 2006–2012 and fluctuated upward from 2013–2019. The trend of regional CE showed “high in the east and low in the west”. (2) The “inverted U” model accurately reflects the relationship between national CE and URID, with a “U” shaped association in the central, western, and northeastern regions, and a positive correlation with consumption level and consumption structure. (3) There is a significant mediating effect of consumption level and structure in the mechanism of URID in regulating CE. Local governments should adopt local policies, take measures to narrow URID and CLD, advocate low-carbon and environmentally friendly living for residents, and promote the upgrading of consumption structure to boost carbon emission efficiency.

Current situation, bottlenecks, and path options for the development of capital flows and integration in the Yangtze River Delta region

IntroductionCapital plays a crucial role in the development of regional economies, especially in low-income regions where it acts as a primary driver of economic growth. Efficient capital flow is essential for optimizing resource allocation and facilitating the development of integrated capital markets. This passage introduces the topic of capital, capital flow, and capital market integration and highlights their significance in regional development.MethodsTo gain a comprehensive understanding of capital flow and integration in the Yangtze River Delta region, the researchers conducted a connotative analysis. They constructed indicators from various aspects, including social fixed asset investment, bank capital flow, government transfer payments, social financing structure, and foreign direct investment. By utilizing these indicators, the researchers aimed to assess the current situation and identify bottlenecks related to capital flow and integration. Additionally, they drew on experiences from foreign capital flow and integration development to enrich their analysis.ResultsThe analysis revealed several primary bottlenecks affecting capital flow and integration in the Yangtze River Delta region. These bottlenecks include an unsound banking management system, the presence of government competition and administrative barriers, and shortcomings within listed companies. The results highlight the specific challenges that hinder the smooth functioning and integration of capital in the region.DiscussionTo promote the development of capital flows and integration in the Yangtze River Delta region, the researchers propose various recommendations. These suggestions include promoting the development of listed companies, establishing a robust banking management model, improving relevant government policies, and optimizing the investment environment. These recommendations serve as important guidelines for policymakers to enhance capital flow and integration in the Yangtze River Delta city cluster. Furthermore, they emphasize the need to strengthen financial supervision and improve institutional mechanisms within the three provinces and one city comprising the region.

Spatial impact of digital finance on carbon productivity

Low carbon productivity has been identified as a key direction for China’s future development. As an important driving force for economic growth, the question of whether digital finance that is reliant on digital technology can support the development of a low-carbon urban economy remains unresolved. Based on the carbon productivity measured by panel data from 201 cities for the period 2011–2020, this study applies the spatial Dubin model and threshold regression model to explore the impact of digital finance on carbon productivity, yielding the following key conclusions. First, the spatial distribution heterogeneity of carbon productivity in China’s eastern region is higher than that in the western region, and both productivity and digital finance are characterized by high (low)–high (low) dotted spatial agglomeration. Second, digital finance can significantly improve carbon productivity via two transmission channels: the human capital and marketization effects. At the same time, digital finance exerts a spatial spillover effect on carbon productivity, and rising local digital finance levels will increase carbon productivity in neighboring areas. Heterogeneity analysis indicates that the spillover effect of digital finance in urban agglomerations and eastern regions is more significant. Third, fixed-asset investment has a positive nonlinear moderating effect on digital finance, thus improving carbon productivity. When the per capita investment in fixed assets does not exceed 682.73 yuan, digital finance exerts only a limit pulling effect on carbon productivity; when it is higher than this value, the pulling effect is intensified.

Regularized Poisson regressions predict regional innovation output

AbstractRegional innovation output is influenced by many factors such as macroeconomic environments, residents consumption, fixed asset investment, foreign trade, fiscal revenue and expenditure, education, and research and development (R&D) input. Correctly predicting regional innovation output is an important subject in the economic field. In this paper, we propose four regularized Poisson regressions to forecast regional innovation output for 31 provinces in China. Firstly, we screen out 20 important factors and combine with four penalties: ridge penalty , least absolute shrinkage and selection operator penalty (LASSO), smoothly clipped absolute deviation penalty (SCAD), and minimax concave penalty (MCP) to construct four regularized Poisson regressions. Secondly, we introduce the cyclic coordinate descent (CCD) algorithm and the training set to complete variable selection and obtain the least squares weighted iterative estimators and make model selection by introducing three criterions to compare goodness of fit to different models. Finally, we apply the testing set and the learned regressions to exhibit the prediction performances and found that SCAD/MCP regularized Poisson regression predicts better than /LASSO regularized Poisson regression in terms of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In particular, MCP regularized Poisson regression outperforms the other three regularized Poisson regressions in predicting the number of granted patents in the three regions.

Spatial differences and influence mechanisms of construction land development intensity in China, 2002–2020

Construction land development intensity is a spatial mapping of modern urbanization level, which integrally reflects urban development strategy, land use efficiency, and population carrying intensity. This article analyzed the spatial and temporal evolution of construction land development intensity using panel data of 31 provincial administrative divisions in China from 2002 to 2020, with the application of the Theil index and spatial autocorrelation. To further investigate the relationship between human activities and land development, the article used geographic detectors to analyze the influencing mechanisms. The results showed that: (1) The average intensity of construction land development of Chinese provinces from 2002 to 2020 showed a trend of "steady increase, a short decline, and then a steady increase," and there were significant differences in the characteristics of construction land development intensity changes in different regions. (2) The regional differences in construction land development intensity between provinces showed a decreasing trend. There were uneven differences among regions, with more minor regional differences in Central, South, and North China but more significant differences in Northwest, East, Southwest, and Northeast China. (3) The spatial agglomeration of construction land development intensity in the region increased initially and then decreased during the study period. The overall pattern was "small agglomeration and large dispersion." (4) Economic development factors such as GDP per land, industrial structure, and fixed asset investment completion significantly affect land development intensity. The interaction between the factors was apparent, and the effect of “1 + 1 > 2” was produced. Based on the study's results, it is suggested that scientific regional development planning, guiding inter-provincial factor flow, and rational control of land development efforts are the key to promoting sustainable regional development.

Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks

ABSTRACT The paper analyzes the sustainability of the Chinese transportation sector by examining the relationship between energy consumption (and CO2 emissions), transportation modes, and macroeconomic variables. Principal Component Analysis (PCA) and Neural Networks (NN) are combined using monthly data from January 1999 to December 2017. Our goal is to propose a model that links China's transportation footprint to major macroeconomic factors while simultaneously controlling each mode of transportation. Inflation and credit policies exert relatively weak effects on the explained variable. In contrast, trade and fixed asset investments, as well as monetary and fiscal policies, show a positive and significant impact. The use of waterways and airways plays an imperative role in sustainable development compared to the use of roads.