Overlooked uneven progress across sustainable development goals at the global scale: Challenges and opportunities

Differences in progress across sustainable development goals(SDGs)are widespread globally;meanwhile,the rising call for prioritizing specific SDGs may exacerbate such gaps.Nevertheless,how these progress differences would influence global sustainable development has been long neglected.Here,we present the first quantitative assessment of SDGs’progress differences globally by adopting the SDGs progress evenness index.Our results highlight that the uneven progress across SDGs has been a hindrance to sustainable development because(1)it is strongly associated with many public health risks(e.g.,air pollution),social inequalities(e.g.,gender inequality,modern slavery,wealth gap),and a reduction in life expectancy;(2)it is also associated with deforestation and habitat loss in terrestrial and marine ecosystems,increasing the challenges related to biodiversity conservation;(3)most countries with low average SDGs performance show lower progress evenness,which further hinders their fulfillment of SDGs;and(4)many countries with high average SDGs performance also showcase stagnation or even retrogression in progress evenness,which is partly ascribed to the antagonism between climate actions and other goals.These findings highlight that while setting SDGs priorities may be more realistic under the constraints of multiple global stressors,caution must be exercised to avoid new problems from intensifying uneven progress across goals.Moreover,our study reveals that the urgent needs regarding SDGs of different regions seem complementary,emphasizing that regional collaborations(e.g.,demand-oriented carbon trading between SDGs poorly performed and well-performed countries)may promote sustainable development achievements at the global scale.

Research on Regional Differences and Influencing Factors of China's Carbon Emissions

This paper studies the regional differences,dynamic evolution and influencing factors of regional carbon emission intensity(CEI)in 262 cities and 5 regional urban agglomerations(UAs)in China.The Dagum Gini coefficient is used to analyze the intra-regional and inter-regional differences in carbon emissions,and the temporal evolution of the absolute differences of CEI among regions is analyzed by means of kernel density estimation(KDE).The paper provides an in-depth study on the spatial difference and temporal evolution of CEI in Chinese cities and major strategic regions.Through Moran index and LISA’s test,the spatial correlation of carbon emission in prefecture-level cities is tested,and its spatial agglomeration characteristics are described.It is found that China’s CEI is decreasing year by year,presenting a spatial pattern of“low in the south but high in the north”.Based on the calculation of carbon emission intensity at the urban level,this paper conducts LDMI factor decomposition research on carbon emission intensity at the national and key regions,and analyzes the impact of the impact factors on carbon emission intensity.The research results provide a path for China’s green development at the city level and urban agglomeration level,and a theoretical support for different regions and cities to introduce emission and carbon reduction policies.

Measuring China's Real Estate Financial Innovation from the Perspective of Government,Enterprises and the Public:Index Compilation and Its Spatial-Temporal Characteristics Analysis

In recent years,China has witnessed the rapid development in housing finance,and there have emerged constantly real estate finance innovations;however,there exists no relevant index for measuring the innovations of China's real estate finance.Based on the perspectives of the governments,enterprises and the public,this paper constructs the"innovation index of real estate finance"on a quarterly basis from 2009 to 2019,with the method of empowerment which combines the subjective method(analytic hierarchy process)and the objective one(range coefficient method).It clearly and concretely depicts the innovations in housing finance and the related temporal-spatial characteristics in China since the outbreak of the financial crisis in 2008.The index covers 30 provinces,autonomous regions and municipalities directly under the central government,and analyzes its temporal and spatial characteristics.The findings show that there exist a strong spatial autocorrelation and a big regional difference in innovations.

Research on Prediction and Early Warning of A-Share Market Volatility Based on HAR-Type Models

Based on the different premium volatility characteristics of various systematic factors in the A-share market, this paper constructs six representative high-frequency volatility prediction models that consider multiple complex risk structures. On this basis, a detailed comparative analysis of the differences in volatility characteristics among various factors is conducted, and the optimal prediction and early warning framework for the A-share market is proposed. Research shows that: 1) The volatility research results only for individual market indexes are not universally representative. 2) The fluctuation characteristics among different systematic factors and their respective optimal prediction model frameworks generally have significant differences, that is, there is no single fixed combination of model parameters. 3) Complex risk characteristics such as long memory, measurement errors, and high-frequency jump fluctuations obviously exist in the A-share market. The optimal forecast and early warning framework for the A-share market can be constructed by a combination of models that consider one or more of the above risk characteristics. The above conclusions have important practical reference value for the risk warning and prevention of the A-share market and the formulation of related policies.

知识嵌入式图神经网络在风机多元状态预测中的应用

风机状态预测是风电数字化和智能运维的关键环节.深度学习由于在挖掘复杂高维数据隐藏关系上具有强大潜力,逐渐被应用于风机状态预测中.然而深度学习在实际运行中也存在着推理性和解释性差等局限性,如何将领域知识与智能算法有效结合是智能运维的一个重要方向.本文以多元时间序列图神经网络通用框架(multivariate time series graph neural network,MT-GNN)为基础,提出了一个知识嵌入式图神经网络模型(knowledge-embedded graph neural network,K-GNN),对风机多元时间序列状态数据进行预测.在该模型中,本文将知识嵌入模块与图学习模块相结合,通过嵌入相关、因果、专家经验3种知识矩阵,更好地刻画出状态变量之间的关联关系.结果显示,在3种知识嵌入式K-GNN模型中,嵌入了专家经验矩阵的图神经网络模型在预测上的表现更为出色,说明领域知识能够有效提升图神经网络模型的泛化性能和可解释性.本文的研究成果对于风电预测性维护技术的研发和推广具有参考意义.

A Study on the Relationship Between Land Finance and Housing Price in Urbanization Process: An Empirical Analysis of 182 Cities in China Based on Threshold Panel Models

Scholars have a variety of theoretical explanations for housing price growth. However, few scholars have studied the internal influence mechanism among urbanization, land finance, and housing price. Based on the data of 182 prefecture-level cities from 2009 to 2016, this paper studies the influence of land finance on housing price under different urbanization rate levels. The study finds that with the increase of urbanization rate, the effect of land finance on housing price presents a "U" shape.Specifically, an increase in land finance by 1% results in a corresponding increase in average housing price by 0.18%, with relatively low urbanization rate, 0.06% with medium level of urbanization rate,and 0.38% with high level of urbanization rate.

The Effect of Industrial Structure Change on Carbon Dioxide Emissions: A Cross-Country Panel Analysis

Reduction of carbon dioxide(CO2)emissions is one of the biggest challenges for global sustainable development,in which economic growth characterized by industrialization plays a formidable role.We innovatively adopted the input and output(I-O)table of 41 countries released by World I-O Database to determine the industrial structure change and analyze its impact on CO2 emission evolution by developing a cross-country panel model.The empirical results show that industrial structure change has a significantly negative effect on CO2 emissions;to be specific,0.1 unit increase in the linkage of manufacturing sector and service sector will lead to a decrease of 0.94 metric tons per capita CO2 emissions,indicating that upgrading industrial structure contributes to carbon mitigation and sustainable development.Further,urbanization,technology and trade openness have significantly negative impact on CO2 emissions,while economy growth and energy use take positive impacts.In particular,a 1%increase in per capita income will contribute to an increase of 8.6 metric tons per capita CO2 emissions.However,the effect of industrial structure on environment degradation is moderated by technology level.These findings fill the gaps of previous literature and provide valuable references for effective policies to mitigate CO2 emissions and achieve sustainable development.

Do Media Reports Matter to House Price Expectations?Evidence from Beijing

House price expectations(HPE)is a key factor affecting housing market fluctuations.Taking Beijing as an example,this study innovatively proposes an internet data-based measurement of HPE and text analysis-based quantification of media reports from media attention and media attitudes.Regression model is performed to empirically test the impact of media reports on the level and accuracy of HPE.The empirical results show no significant relationship between media attention and the level of HPE,but a significant relationship to the accuracy of HPE.The higher the media attention(i.e.,the more intensive the media reports),the smaller the deviation between HPE and actual housing prices.The attitude of media reports is significantly related to the level and accuracy of HPE.It is easier to guide the formation of HPE through media reports with clear opinions,indicating that media could promote the sustainable development of the real estate market.

An Empirical Research on Metropolitanization and Housing Price Differentiation Based on the Data of 31 Provincial Capitals and Municipalities with Independent Planning Status in China

Under the background of population aggregation in megacities,some adjustments are made to the urbanization strategy,whose focus is shifted to the development of megacities and megacity clusters.Meanwhile,the housing price differentiation among cities tends to become increasingly serious.This paper,from the perspective of population mobility,takes provincial capitals and municipalities with independent planning status(PCs&MIPSs)as research samples to evaluate the level of housing price differentiation within provincial-level administrative divisions of China,and analyze from the perspective of demand side how the metropolitanization effects regarding the population formed due to population aggregation in megacities affect the housing prices of megacities and the housing price difference between megacities and other cities.The research found that:1)The increasing net inflow of population boosts the housing prices and accelerate the housing price differentiation;2)The impact of the increasing net inflow of population on housing price increases and housing price differentiation has regional heterogeneity and city size heterogeneity;3)The income gap strengthens the effect of population inflow upon the housing price differentiation.