Spatial spillover effects of official development assistance on environmental pressure in sub-Saharan African(SSA)countries

Impoverished sub-Saharan Africa(SSA)is under increasing environmental pressure from global environmental changes.It is now generally accepted in academic circles that economic development in SSA countries can cause environmental pressure in other countries.However,there is research gap on the impact of economic assistance on environmental pressure in SSA countries and whether economic assistance causes spatial spillovers of environ-mental pressure between SSA countries.To better understand the impact of economic assistance on environmental pressures in SSA,a dynamic spatial Dubin panel model was developed.It helped us explore the spatial spillover effects of economic assistance on environmental pressures in recipient countries based on the panel data from 34 SSA countries.The results show that economic assistance had a positive stimulating effect on environmen-tal pressures of recipient countries,which means that the degree of human disturbance to the environment has deepened.Due to the regional correlation effect,neighboring countries were saddled with environmental pres-sures from the target country.Moreover,environmental pressures have time inertia,which can easily produce a snowball effect.The decomposition of effects shows that the impact of economic assistance on environmental pressures is relatively minor.Environmental pressures have spillover effects,so to deal with diffuse risks,joint regional prevention and control policies should be developed.

Brent vs.West Texas Intermediate in the US petro derivatives price formation

In this paper,we apply the spatial panel model to explore the relationship between the dynamic of two types of crude oil prices(WTI and Brent crude oil)and their refined products over time.Considering the turbulent months of 2011,when Cushing Oklahoma had reached capacity and the crude oil export ban removal in 2015 as breakpoints,we apply this method both in the full sample and the three resultant regimes.First,results suggest our results show that both WTI and Brent display very similar behaviour with the refined products.Second,when attending to each regime,results derived from the first and third regimes are quite similar to the full sample results.Therefore,during the second regime,Brent crude oil became the benchmark in the petrol market,and it influenced the distillate products.Furthermore,our model can let us determine the price-setters and price-followers in the price formation mechanism through refined products.These results possess important considerations to policymakers and the market participants and the price formation.

Unveiling the impact and mechanism of digital technology on agricultural economic resilience

Enhancing the economic resilience of agriculture is essential for promoting sustainable and high-quality agricultural development.The emergence of digital technology has created new opportunities in this field.However,existing research predominantly focuses on traditional agricultural factors and technologies.Therefore,the impact of digital technology on agricultural economic resilience within the broader context of the“production-operation-industry”system in agriculture has not been comprehensively explored.To bridge this gap,this study analyzes panel data from 30 Chinese provinces from 2011 to 2020.It employs the static Van Dorn’s law and a dynamic spatial panel model to examine how digital technology empowers agricultural resilience.The findings indicate a continuous strengthening of digital technology development in China,albeit with significant polarization and spatial imbalances.Moreover,the resilience of the agricultural economy undergoes notable fluctuations,initially narrowing and subsequently displaying an upward trend.Digital technology clearly plays a pivotal role in empowering resilience through agricultural scale operation,industrial transformation,and technological progress.Its impact,particularly on the promotion of resilience in the eastern region and non-grain-producing areas and on high-level agricultural economies,also shows regional and technological variations.

The Core Driver of China＇s Economic Growth： An Analysis Based on Cross- Region Spillover Effects of Factors

China＇s recent economic slowdown has provoked academic discussion on what the core driver should be to ensure sustainable and healthy economic growth. To answer this question, it is essential to analyze the resources allocation efficiency. Using the newly-developed spatial panel data model, this paper studies not only the direct efject of resources allocation on local economies, but also the spillover effects on the economies in other regions. We are then able to assess the actual effects of resource factors on China＇s economic growth, including labor force, fixed-asset investment （FAI） and technical progress. The conclusions are.＂ 1） during the past 13 years, the labor force has had an insignificant effect on all the three industries; 2） FAI has produced a prominent positive direct effect on secondary industry, but accompanied with even severer negative spillover effects which outweigh the positive ones; however, FAI has had significant direct and total effect on the tertiary industry,＂ 3） technical progress has significant effects on all the three industries. Therefore, the labor force is not the core driver of China＇s economic growth, and the demographic dividend is an invalid explanation for China＇s economic growth. Instead, fixed-asset investment remains the powerhouse of China＇s economic growth, not in the secondary industry but in the tertiary industry. All in all, technical progress is the core driver for healthy economic growth as well as the inevitable path to industrial upgrading in China.

Bayesian Estimation and Model Selection for the Spatiotemporal Autoregressive Model with Autoregressive Conditional Heteroscedasticity Errors

The spatial and spatiotemporal autoregressive conditional heteroscedasticity(STARCH) models receive increasing attention. In this paper, we introduce a spatiotemporal autoregressive(STAR) model with STARCH errors, which can capture the spatiotemporal dependence in mean and variance simultaneously. The Bayesian estimation and model selection are considered for our model. By Monte Carlo simulations, it is shown that the Bayesian estimator performs better than the corresponding maximum-likelihood estimator, and the Bayesian model selection can select out the true model in most times. Finally, two empirical examples are given to illustrate the superiority of our models in fitting those data.

Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014-2021

Background Tuberculosis(TB)remains a pressing public health issue,posing a significant threat to individuals'well-being and lives.This study delves into the TB incidence in Chinese mainland during 2014-2021,aiming to gain deeper insights into their epidemiological characteristics and explore macro-level factors to enhance control and prevention.Methods TB incidence data in Chinese mainland from 2014 to 2021 were sourced from the National Notifiable Disease Reporting System(NNDRS).A two-stage distributed lag nonlinear model(DLNM)was constructed to evaluate the lag and non-linearity of daily average temperature(℃,Atemp),average relative humidity(%,ARH),average wind speed(m/s,AWS),sunshine duration(h,SD)and precipitation(mm,PRE)on the TB incidence.A spatial panel data model was used to assess the impact of demographic,medical and health resource,and economic factors on TB incidence.Results A total of 6,587,439 TB cases were reported in Chinese mainland during 2014-2021,with an average annual incidence rate of 59.17/100,000.The TB incidence decreased from 67.05/100,000 in 2014 to 46.40/100,000 in 2021,notably declining from 2018 to 2021(APC=-8.87%,95%CI:-11.97,-6.85%).TB incidence rates were higher among males,farmers,and individuals aged 65 years and older.Spatiotemporal analysis revealed a significant cluster in Xinjiang,Qinghai,and Xizang from March 2017 to June 2019(RR=3.94,P<0.001).From 2014 to 2021,the proportion of etiologically confirmed cases increased from 31.31%to 56.98%,and the time interval from TB onset to diagnosis shortened from 26 days(IQR:10-56 days)to 19 days(IQR:7-44 days).Specific meteorological conditions,including low temperature(<16.69℃),high relative humidity(>71.73%),low sunshine duration(<6.18 h)increased the risk of TB incidence,while extreme low wind speed(<2.79 m/s)decreased the risk.The spatial Durbin model showed positive associations between TB incidence rates and sex ratio(β=1.98),number of beds in medical and health institutions per 10,000 population(β=0.90),and total health expenses(β=0.55).There were negative associations between TB incidence rates and population(β=-1.14),population density(β=-0.19),urbanization rate(β=-0.62),number of medical and health institutions(β=-0.23),and number of health technicians per 10,000 population(β=-0.70).Conclusions Significant progress has been made in TB control and prevention in China,but challenges persist among some populations and areas.Varied relationships were observed between TB incidence and factors from meteorological,demographic,medical and health resource,and economic aspects.These findings underscore the importance of ongoing efforts to strengthen TB control and implement digital/intelligent surveillance for early risk detection and comprehensive interventions.

Relationship between Urban Industrial Land Use Size and Sulfur Dioxide Emissions:Evidence from 294 Cities in China

Although the relationship between the size of urban industrial land use and pollutant emissions has been widely discussed from different perspectives(e.g.,the scale and crowding effects),the results of various studies have revealed positive,negative,and combined impact relationships.However,how the expansion of urban industrial land use affects SO_(2) emissions remains unknown.We need to clarify this relationship in order to facilitate the realization of China’s pollution reduction and emission reduction goals.This study used the panel data from 294 cities spanning from 2011 to 2019 to construct a spatial econometric model.The objective was to explore the correlation between the scale of urban industrial land and sulfur dioxide emissions in China.The results show that a large scale of urban industrial land use corresponds to lower sulfur dioxide emissions per unit of industrial added value.By gaining a deeper understanding of the relationship between the scale of urban industrial land use and sulfur dioxide emissions,policymakers can further reduce pollutant emissions by rationalizing the planning of urban industrial land use and industrial layout.In addition to promoting industrial agglomeration and economies of scale in cities with extensive industrial land use,this strategy can support the development of efficient and environmentally friendly industries in areas with limited industrial land use.Optimizing the technology and encouraging the development of green industries can help reduce environmental pollution and promote sustainable urban development.

Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province,China:a spatial clustering panel analysis

Background:Tuberculosis(TB)is the notifiable infectious disease with the second highest incidence in the Qinghai province,a province with poor primary health care infrastructure.Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB.Methods:Our TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks,respectively.We calculated the global and local Moran’s I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year.A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation.Results:The Local Moran’s I method detected 11 counties with a significantly high-high spatial clustering(average annual incidence:294/100000)and 17 counties with a significantly low-low spatial clustering(average annual incidence:68/100000)of TB annual incidence within the examined five-year period;the global Moran’s I values ranged from 0.40 to 0.58(all P-values<0.05).The TB incidence was positively associated with the temperature,precipitation,and wind speed(all P-values<0.05),which were confirmed by the spatial panel data model.Each 10°C,2 cm,and 1 m/s increase in temperature,precipitation,and wind speed associated with 9%and 3%decrements and a 7%increment in the TB incidence,respectively.Conclusions:High TB incidence areas were mainly concentrated in south-western Qinghai,while low TB incidence areas clustered in eastern and north-western Qinghai.Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences.

Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.

“Competition for Innovation”:Research on the Strategic Competition of Local Governments'Science and Technology Expenditure in China

By constructing a yardstick competition model of the performance equation,this paper analyzes the internal mechanism of local governments'competition of science and technology(S&T)expenditure in China.By using the Dynamic Spatial Panel Model,we analyze the competition empirically from five dimensions:geographical adjacency,geographical distance,administrative adjacency,administrative grade and economic distance.According to the results,the local governments'S&T expenditure showed a significant strategic competition phenomenon,that is,there is a“competition for innovation”,and the competition is more obvious between regions with close geographical proximity and administrative relations.“Competition for innovation”will be affected by political exogenous shocks such as those from the National People's Congress(NPC)of China;There is a“father-son competition”relationship between the superior and inferior governments.At last,the competition can promote the regional innovation significantly,which shows that“competition for innovation”is a kind of top-by-top competition.The conclusions may provide some useful suggestions for optimizing the relationship between local governments and promoting the construction of an innovative country.

Spatiotemporal dynamics of carbon intensity from energy consumption in China

The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%-45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spati- otemporal dynamics and dominating factors of China＇s carbon intensity from energy con- sumption in 1997-2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China＇s carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP （11.72%）. Secondly, the trend of Moran＇s I indicated that China＇s carbon intensity has a growing spatial agglom- eration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel economet- ric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China＇s carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.