Reasonable Policy Research Articles

Influences of the Plot Area and Floor Area Ratio of Residential Quarters on the Housing Vacancy Rate: A Case Study of the Guangzhou Metropolitan Area in China

Factors affecting the housing vacancy rate (HVR) vary, but few studies have considered the relationships between the HVR and plot area (PA) and floor area ratio (FAR). This study thus considered 212 residential quarters in the Guangzhou metropolitan area as the research object, and we constructed a regression model of the factors impacting housing vacancies. The model includes two explanatory variables, PA and FAR, and the remaining six impact factors as control variables. In this study, the influences of PA and FAR on the HVR was analyzed by combining the traditional ordinary least squares (OLS) and two spatial regression models: the spatial lag model (SLM) and spatial error model (SEM). The results indicate that (1) the HVR in the Guangzhou metropolitan area shows spatial difference characteristics of the low central area and high edge, and there is spatial autocorrelation. (2) The PA of the residential quarters gradually increases from the central to the edge area, but the spatial pattern of FAR is the opposite. (3) The SLM results indicate that the PA and FAR of the residential quarters have significant positive correlations with HVR; that is, the larger the PA and FAR, the larger the HVR of the residential quarters, which is in accordance with the expected direction of the theory; furthermore, basic education convenience, road density, and waterfront accessibility have significant negative effects on HVR. This conclusion provides a reference for government departments to formulate reasonable and effective housing policies aimed at the current housing vacancy problem and should help alleviate urban housing vacancies.

Spatial distribution characteristics of rare and endangered medicinal plant resources in Gansu province

Gansu province is located at the intersection of the three plateaus(Qinghai-Tibet Plateau, Inner Mongolia Plateau, and Loess Plateau) and the three river basins(Yellow River Basin, Yangtze River Basin, and inland river basin). The complex eco-environment and climate conditions here have created rich and diverse vegetation. Therefore, it is of great significance to study the spatial distribution characteristics of rare and endangered medicinal plant resources in Gansu province for formulating reasonable protection po-licies and promoting the development of medicinal plant industry. The data of rare and endangered medicinal plant resources in 87 counties of Gansu province were collected from results of the fourth general survey. The spatial distribution and the high-or low-value gathering area of rare and endangered medicinal plant resources in Gansu province were analyzed by geostatistical methods such as exploratory spatial data analysis, trend surface analysis, and Anselin Local Moran's I. The eco-environment characteristics of the high-or low-value gathering area were analyzed with the data of vegetation type, soil texture classification, annual mean temperature, annual mean precipitation, and elevation. Furthermore, the relationships of the spatial distribution and diversity with the geographical environment of rare and endangered medicinal plants in Gansu province were analyzed to provide support for the restoration and protection policy making of these plant resources.

The Spatial Economic Effect of Industrial Credit Misallocation in China

The cross-industry allocation of bank credit plays an important role in China’s economic development and optimizing and upgrading industrial structures. This article collects the panel data of 19 industries in China from 2007 to 2017, constructs dynamic spatial panel models, and deeply analyzes the impact of credit misallocation on industry economy. The results show that structural misallocation of bank credit exists in various industries in China. Particularly, credit deviation degree of China’s industries has gradually declined, but structural volatility remains large. A large amount of capital flows to the tertiary industry, while the capital of the primary industry and the secondary industry is relatively scarce. What is more, credit misallocation has a negative impact on industry economic growth and shows a weak acceleration effect. Therefore, in formulating economic policies, the government should actively guide the rational flow of inter industry credit funds and improve the efficiency of capital allocation. The government should continue to increase investment in manufacturing and promote independent innovation. Additionally, reasonable financial policies should be formulated to ensure the effective operation of the market and reduce the degree of credit misallocation.

Averting Tragedy: An Exploration of Thwarted Mass Public Shootings Relative to Completed Attacks

Scholarship on mass public shootings has increased in recent years as comprehensive datasets have become more available. As a result, much is known about the contextual and offender related characteristics of such attacks. However, less research has been conducted on attacks that were planned but ultimately did not occur. Understanding how mass public shootings may be thwarted or averted is important for both policy and theoretical reasons. In this paper, we describe a new dataset of averted mass public shooting threats (N = 194) from 2000–2019 and compare them to mass public shootings that were completed during this time (N = 97). Several noteworthy findings emerged, including that nearly half of the averted cases were reported by a friend or acquaintance, most targeted a specific location or group, and averted cases were more likely to involve school targets and co-offenders. Implications are discussed.

Hard budget constraints and artificial intelligence technology

Motivated by the contradiction between a government's hard budget constraints and artificial intelligence, this study constructs a computable general equilibrium model embedded with various fiscal and tax policies to study the impact of artificial intelligence development on the Chinese economy under government budget constraints with different intensities. This paper seeks to find a reasonable policy that takes into account China's employment, income distribution, and budget constraints to achieve common prosperity. It finds that the softer the government's budget constraints, the smaller the negative impact of artificial intelligence on the economy. More specifically, allowing the government to increase its debts and spending is more effective than tax cuts. It is suggested that if the goal is to reconcile the contradiction between hard budget constraints and artificial intelligence, fiscal and tax policy combinations, together with an improvised soft budget constraint, are required to increase the taxation of capital to an appropriate degree. In order to resolve the contradiction between the government's hard budget constraints and the development of artificial intelligence in pursuit of common prosperity, a robot tax should be levied and automation capital taxed.

Research on the Impact of Population Aging on High-quality Economic Development in China

This paper first analyzes the current situation and characteristics of China's population aging, and analyzes the impact mechanism of population aging on China's high-quality economic development from the five levels of innovation, coordination, green, openness and sharing. Secondly, the entropy weight method is used to calculate the high-quality development level of China's economy. Thirdly, this paper uses Eviews software to carry out the experimental results. The results show that the population aging has a certain role in promoting the high-quality economic development. Finally, based on the results of empirical analysis, reasonable policy suggestions are put forward.

Humanizing the Physician-Patient Relationship

Humanizing the Physician-Patient Relationship

The Nitrogen Removal Ability of Salt Marsh Improved After Grazing Prohibition

Grazing prohibition has been used for some conservation purposes in salt marshes. However, the impact of this measure on microbe-mediated key nitrogen removal processes remains poorly understood. Therefore, this study assessed the impact of grazing prohibition on potential rates of nitrification and denitrification under short- and long-term grazing prohibition in high and middle elevation of the Dongtan salt marsh on Chongming Island, China. Compared with short-term grazing prohibition, we found that long-term grazing prohibition significantly increased nitrification and denitrification (except for nitrification in the high marsh), which indicates that the nitrogen removal ability of the salt marsh was improved along with the grazing prohibition time. Furthermore, we found that nitrification and denitrification in the high marsh were largely affected by NH4+ and soil moisture, respectively. Nitrification and denitrification in the middle marsh were mainly controlled by soil organic carbon (SOC) and nirS gene abundances, respectively. Our results indicate that the implementation of scientific and reasonable grazing prohibition policies in salt marshes has great potential to restore their ecosystem functions, maintain their ecosystem balance and realize their sustainable development.

Changes in Extreme Temperature Events and Their Contribution to Mean Temperature Changes during Historical and Future Periods over Mainland China

Extreme climate events undoubtedly have essential impacts on terrestrial ecosystems, but the spatiotemporal patterns of extreme climate events at regional scales are unclear. In this study, based on observations and 14 CMIP6 global climate models, we analyzed the spatiotemporal changes in extreme temperature events at the mainland China scale and different basin scales in historical and future periods, and their relative importance for the changes in mean temperature (Tmean). The results show that at the mainland China scale in the historical period, extreme cold days and extreme cold nights significantly decreased, while Tmean, extreme warm days, and extreme warm nights significantly increased. However, the rates of increase in Tmean and extreme temperature events in the Continental Basin, Southwest Basin and Yellow River Basin are higher than that at the mainland China scale. The multi-model ensemble is the best model for simulating extreme temperature events in mainland China. At the mainland China scale in the future, the trends of Tmean and extreme temperature events are slow, rapid, and extremely rapid under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively. In addition, the changes in the Continental Basin and Songhua and Liaohe River Basin are larger than those at the mainland China scale. In the historical and future periods, the extreme temperature events that have a great influence on the Tmean at the Chinese mainland scale and different basin scales are all related to the minimum temperature. The findings from this study can provide references for formulating scientific and reasonable regional-scale climate change policies.

Forecasting clean energy generation volume in China with a novel fractional Time-Delay polynomial discrete grey model

Clean energy, represented by hydropower, wind power, and nuclear power, plays a crucial role in addressing the issue of power shortage and promoting the transition of the energy structure. Scientific and accurate forecasting of clean energy generation volume can provide the necessary reference for relevant authorities to formulate reasonable energy policies. This paper proposes a novel fractional time-delay polynomial discrete grey model, FTDP-DGM(1,1), for forecasting clean energy generation volume in China. Firstly, both the structural parameters and the time response function are derived directly from the discrete equation. Secondly, the genetic algorithm is employed to determine the optimal value of the nonlinear parameter in the model, with the optimization objective of minimizing the mean absolute percentage error. Then, the proposed model is applied to the simulation and prediction of hydro, wind, and nuclear energy generation volume in China. The results show the validity and superiority of the model. Finally, out-of-sample forecasts are performed, and the forecasting results show that the proposed model has good performance with values of 1.29%, 2.94%, and 2.32% for mean absolute percentage error(MAPE), respectively. It is expected that in 2021–2025, China's hydro, wind, and nuclear power generation volume will continue to grow at a rate of 1.45%, 10.74%, and 10.66% respectively. Based on the forecast results, policy recommendations are suggested.

How Asteroid Mining Affects Global Equity

The shortage of mineral resources on Earth is worsening. As a promising form of resource extraction, asteroid mining has technical problems and faces capital challenges. The arrival of asteroid mining is a crucial opportunity to improve global equity. The development of reasonable mining methods and policies can enable asteroid mining to reshape the global economic landscape, which will greatly promote global equity. Firstly, we give a concrete definition of global equity and explain the meaning of the different terms in the definition. Then we quantitatively assess global equity through four dimensions: resources, economy, living conditions, and degree of the rule of law. In order to quantify these four abstract concepts, we introduce a total of 14 different indicators. Then, we design a form of international cooperative mining for the asteroid mining industry in which we build a resource allocation model using national research capacity and research inputs as measures. The model follows the principle of output distribution by input, allowing international companies to extract asteroid minerals, which are then distributed in three ways: company operations, input charity, and shareholder income. Next, we clustered the indicators affecting this international mining company and finalized three conditions. We use these as indicators of changes in the conditions affecting the company. Finally, we discuss the difficulties faced in asteroid mining and the policies that should be implemented based on the results obtained.

Estimating monthly global ground-level NO2 concentrations using geographically weighted panel regression

Predicting long-term ground-level nitrogen dioxide (NO2) is important globally to support environmental and public health research and to provide information to governments and society for air pollution control policies. The ozone monitoring instrument (OMI), onboard Aura Satellite, detects monthly global tropospheric column amounts (TrCA) of NO2 molecules. However, the relationship between the ground-level NO2 concentration and TrCA of NO2 molecules remains elusive because NO2 molecules in the air are not evenly distributed vertically. We use geographically weighted panel regression (GWPR) to examine the relationship between satellite-derived data, measured ground-level NO2 concentrations, and several controlling meteorological variables from January 2015 to October 2021. The GWPR can analyze unbalanced panel data and capture the spatial variability of the relationship. Based on the GWPR estimation, 82 monthly global ground-level NO2 concentrations are predicted from January 2015 to October 2021. The GWPR is reliable, as indicated by the 10-fold cross-validation. The accuracy of the raster prediction of global ground-level NO2 from January 2015 to October 2021 is 69.61%. The coefficient of correlation, root mean square error and mean absolute error between globally predicted and measured ground-level NO2 are 0.838, 7.84 μg/m3 and 4.07 μg/m3, respectively, while the mean of globally measured ground-level NO2 is 19.47 μg/m3. Overall, this research provides critical basic data to environmental and public health science and valuable information for governments and societies to make more reasonable policies.

THE IMPACT OF CHINESE ENTREPRENEURS' EMOTIONAL LABOR AND WORK PRESSURE ON EXPORT PROFITABILITY: EVIDENCE FROM CHINA

THE IMPACT OF CHINESE ENTREPRENEURS' EMOTIONAL LABOR AND WORK PRESSURE ON EXPORT PROFITABILITY: EVIDENCE FROM CHINA

Unaccompanied Children in Limbo: The Causes and Consequences of Uncertain Legal Status

Abstract Unaccompanied children seeking international protection often find themselves with forms of discretionary, time-limited, or otherwise uncertain legal status in countries outside their own. This may be due to the available options for legal status or to long delays in determination of protection claims, among other factors. While the legal and policy reasons for this vary, of particular concern is the imposition of such uncertainty to discourage children from persisting with protection claims, to deter others from arriving, to delay status resolution until adulthood, or to facilitate removal. Children possessing a legal status that is insecure or uncertain may be described as being in ‘legal limbo’. This article explores this issue and examines the often deleterious consequences of limbo for unaccompanied children. Through an analysis and comparison of Australia and the United Kingdom, it explains ‘legal limbo’ as a function of States’ desire to deter, control, and punish irregular migration. This article argues that an approach based on rights, as opposed to migration control, must guide the availability of secure legal status to unaccompanied children. To this end, it articulates the basis of an obligation to provide these children such status, drawing on their rights under the Convention on the Rights of the Child and on the commentary of treaty bodies. The article concludes by calling for better pathways to permanent stay for unaccompanied children.

Leadership practices and sustained lesson study

ABSTRACT Background The continual improvement of teaching and learning is vital for schools in order to meet the rapid changes in the world around us. Lesson study is considered a valuable professional development approach in that regard. It is focused on teachers collaboratively studying live classroom lessons. Sustaining lesson study, by making its core components part of the school’s organisational routines, can help schools continually and systematically to improve student learning and teaching. However, despite the value of this approach, sustaining lesson study turns out to be complex. Leadership seems to play a crucial role therein, but the question is: how? Purpose The study aimed to gain in-depth insight into what leadership looks like in schools that sustained lesson study. Method A case study design was used to investigate leadership in two secondary schools. We conducted over 300 hours of observations through immersion within practice, collected school policy documents, and interviewed the school leadership. Data were analysed qualitatively. Findings The analysis identified that both schools carried out various leadership practices for sustained lesson study. For leadership at schools that aim for sustained lesson study, it seems important to schedule lesson study, to be available, to have knowledge about lesson study and to appoint a lesson study coordinator. Whether and how leadership practices were carried out seemed to depend on the school’s context – for instance, by the policy reasons for working with lesson study. As such, it is important to examine the school and its leadership in context when considering sustainability. Conclusion Through immersion within practice, specific and real-life examples of leadership were identified. This study’s insights could help school leadership to sustain lesson study in their schools.

Labour, left and right: On party positioning and policy reasoning

Since the Second World War, the Labour Party has alternated between periods in which it has been positioned further to the left and others when it has been further to the right. The standard explanation for those oscillations is electoral–ideological. Labour moves to the right (towards the political centre) when it prioritises electoral considerations, usually after a period in opposition. It moves to the left when it prioritises ideology, usually after periods in government. Against this we propose a political economy–policy explanation. Labour moves to the right and a redistributive economic strategy when the economy is performing reasonably well. It moves to the left and adopts a structural economic reform programme when the economy is perceived to be in crisis. We argue that the political economy–policy explanation offers a more satisfactory account of when and why Labour has changed its spatial policy positioning in this period. By acknowledging the role of what we term ‘policy reasoning’ in the processes by which political parties (and the public) adopt and change spatial policy positions, our approach provides an alternative to the mechanistic models deriving from Downsian rational choice theory. It suggests that, contrary to general expectation, the Labour Party under Keir Starmer does not need to react to its 2019 election defeat by shifting significantly to the right.

A More Liberal Public Reason Liberalism

Abstract In recent years, leading public reason liberals have argued that publicly justifying coercive laws and policies requires that citizens offer both adequate secular justificatory reasons and adequate secular motivating reasons for these laws and policies. In this paper, I provide a critical assessment of these two requirements and argue for two main claims concerning such requirements. First, only some qualified versions of the requirement that citizens offer adequate secular justificatory reasons for coercive laws and policies may be justifiably regarded as plausible liberal principles of public justification. And second, the requirement that citizens offer adequate secular motivating reasons for coercive laws and policies is untenable on multiple grounds. Public reason liberals should focus on assessing the justificatory reasons offered for and against coercive laws and policies rather than requiring citizens to offer adequate secular motivating reasons for such laws and policies.

Can Digital Transformation Promote Green Technology Innovation?

Using the index of the degree of digital transformation of enterprises constructed based on text analysis, and combining the data of Shanghai and Shenzhen A-share listed companies from 2007 to 2020, a panel data model was established to empirically study the impact of digital transformation on green technology innovation and the mechanism of action and to further analyze the impact of heterogeneity. The results show that digital transformation can significantly promote green technology innovation, and its internal mechanism is that digital transformation can improve the level of green technology innovation by alleviating financing constraints and attracting government subsidies. Compared with nonstate-owned enterprises and small and medium-sized enterprises, digital transformation plays a more significant role in promoting green technology innovation in state-owned enterprises and large-scale enterprises. Therefore, the government should regulate the market order and formulate reasonable financial policies to provide policy and financial support for enterprises to carry out digital transformation, mobilize the willingness of enterprises to carry out green technology innovation and improve the level of green technology innovation in China.

The Impact of Industrial Intelligence on Energy Intensity: Evidence from China

With the sustainable development of cyber-physical science and information technologies, artificial intelligence technology is becoming more and more mature and has been used widely in various walks of life. As one part of this development, industrial intelligence has been applied diffusely to improve the productivity and energy efficiency of factories and governments. Meanwhile, the social ecological environment change has also caused widespread social concern in recent years, and energy efficiency, which is related to climate change, has forced almost every country to reduce their carbon emissions for bettering environmental quality. However, there is little research that has studied this problem from the perspective of industrial robots, even though they are an indispensable part in modern industrial systems. In order to promote the development of artificial intelligence and its application in industrial fields effectively and raise the energy consumption efficiency of production, this paper investigates the impact of industrial intelligence on energy intensity in China, as it is the largest manufacturing and energy consumption country in the world, and we also hope that the experimental results in this study can guide relevant departments and governments to formulate reasonable policies to enhance the utilization efficiency of energy and improve the environmental quality synchronously. For the sake of the rigor of this research and the accuracy of the experimental results, this study explores the corresponding effect mechanisms of industrial intelligence on China’s energy intensity from 2008 to 2019 by using the classical linear regression model OLS (Ordinary Least Squares) and WLS (Weighted Least Squares) separately, which were applied in the previous studies. The results of this study reveal three major findings. The first is that it further proves that the application of artificial intelligence can indeed reduce energy intensity, and the wide applications of artificial intelligence can reduce energy intensity significantly by reducing energy consumption. Besides, the ownership structure of state-owned enterprises will have a positive impact on energy efficiency. The environmental performance of state-owned enterprises is better than that of foreign-funded and private enterprises. Finally, the models further verify the significant impact of the enterprise scale effect on energy intensity. It will bring about the improvement of economic efficiency, and the larger the enterprise, the more obvious the economies of scale effect and the lower the energy intensity.

Has environmental regulation improved the international competitiveness of Chinese cities?

ABSTRACT Based on the instrumental variable method and a fixed-effects model, this paper uses panel data from 283 prefecture-level cities in China from 2004 to 2016 to analyse the impact of environmental regulation on the international competitiveness of China. The results suggest that (1) the strengthening of environmental regulation hinders the international competitiveness of Chinese cities; (2) technological innovation, resource allocation and government intervention all have partial mediating effects between the intensity of environmental regulation and the international competitiveness of Chinese cities. Nevertheless, if reasonable environmental policies can be formulated to enable the organic coordination of technological innovation, resource allocation and government intervention, it is possible to achieve a win-win situation between environmental regulation and the international competitiveness of Chinese cities.