This paper will focus on the of gravity models, which are important theoretical spatial models. The gravity models are generalization of the natural gravity model in physics but they need to be generalized to have interactions with n regions instead of just two regions. A gravity model in economics concerns flow variables. For estimation of those spatial flow models, we will consider the classical estimation approach.
In recent years, Southeast Asian and South Asian countries have significantly increased their share in the global supply chain, showcasing notable economic resilience and growth potential. During this phase of accelerated economic development, rapid industrialization and urbanization have led to a continuous rise in carbon dioxide (CO2) emissions, exacerbating the challenges of climate change. In this context, emerging economies in Southeast and South Asia must develop tailored emission reduction targets and pathways to mitigate the adverse impacts of climate change effectively. A comprehensive, detailed, and unified CO2 emission inventory serves as a critical foundation for assessing emission trends and formulating strategic planning pathways. To this end, this study integrates multi-scale energy, population, and economic data to construct CO2 emission inventories for 11 countries in Southeast and South Asia from 2010 to 2020. The study highlights the carbon emission heterogeneity across countries at various spatial scales, energy types (including biomass), and disaggregated economic sectors. Furthermore, it provides scientific insights to support developing economies in crafting both short- and long-term energy transition strategies, as well as designing context-specific CO2 emission reduction policies at the national and regional levels.
It is of great value to understand the mechanism of stock return, but China's stock market is affected by many factors at home and abroad, and there are regular behaviors, which makes it diffcult to carry out research. In this paper, a functional varying coeffcient single index model with subgroup structure is proposed and applied to analyze the influencing factors of stock return. The influencing factor system includes three dimensions: macro, mesoscopic and microscopic. At the same time, this paper uses functional data structure to depict calendar effect, and uses subgroup structure to depict industry effect, making the model as comprehensive as possible and suitable for our national conditions. Finally, it is found that the influence direction of macroeconomic factors on stock return varies throughout the year, and the influence of international crude oil price change rate on stock return has industry heterogeneity. At the same time, profitability, value-creating ability and valuation level have great impact on enterprise evaluation and stock return rate. Furthermore, based on some regularity conditions, this paper establishes the asymptotic theory for the estimators of the index parameters, link function, and coeffcient function in the proposed model.
To investigate the impact of the pilot policy of China's carbon emissions trading system (ETS) on the transition risk of provincial financial institutions and its mechanism is an important research topic with practical significance and theoretical value. In this paper, we constructed transition risk indicators for provincial financial institutions, and analyzed the effects of the ETS pilot policy on the transition risk of financial institutions in the pilot areas and its mechanism by using the staggered double difference method (staggered DID), the two-step method of mediation effect analysis, and the generalized method of moments panel vector autoregression model (GMM-PVAR). The study finds that: firstly, the ETS pilot policy increases the transition risk of financial institutions in the pilot region, and the heterogeneity of environmental regulatory policies on the effects of the pilot policy is more significant than that of geographical factors; secondly, the ETS pilot policy mainly works through the channels of direct costs of green transition, business returns, green technological advances, and green credits, among which there is heterogeneity in the mechanism of green technological advances; lastly, the green technological advances and the green credits of enterprises are related to the direct costs of green transition, and there is heterogeneity in the mechanism of green technological advances. Finally, there is a dynamic lag effect between the green technology progress and the enterprise business income mechanism, which makes the ETS pilot policy have a lagging negative impact trend. This paper is the first to explore the inherent dynamic connection between the ETS pilot policy and the transition risk of provincial financial institutions from the level of quantitative analysis, and puts forward relevant suggestions for balancing the implementation of the policy and the risk management of the transition, so as to provide a reference basis for the steady promotion of the "dual-carbon" process and the safeguarding of financial stability in the low-carbon economy.
The "dual carbon" targets represent a significant strategic decision for China's low-carbon economic transformation, carrying profound implications for highquality economic development. This paper first establishes an index system capable of scientifically measuring the progress of China's cities towards achieving the "dual carbon" targets, based on both the absolute level of regional carbon reduction and carbon sink enhancement gaps and the relative level after considering regional population size, energy consumption, and economic development. It then analyzes the patio-temporal evolution characteristics of these gaps, providing a quantitative basis for advancing China's "dual carbon" targets. Subsequently, leveraging the quasinatural experiment of carbon emissions trading pilots and panel data spanning 2006–2020 at the prefecture-level city level, this paper delves into the role of market-based policy instruments in achieving carbon neutrality goals. The research findings indicate that the implementation of carbon emissions trading policies contributes to advancing carbon neutrality in pilot regions across four dimensions: Regionally overall, per capita, in terms of energy consumption, and economic development. The mechanism analysis reveals that this policy fosters carbon neutrality through multiple pathways, including carbon emission reduction, carbon sequestration enhancement, and green innovation. Specifically, the policy aids in optimizing energy structures and enhancing energy effciency at the emission end, promotes afforestation and forest conservation at the carbon sequestration end, and exhibits a "Porter Effect" that stimulates quantitative growth in green innovation in pilot regions. Further research demonstrates that a well-functioning carbon market can amplify the emission reduction and carbon sequestration enhancement effects of carbon emissions trading policies. By breaking down market mechanisms into three aspects, carbon price, liquidity, and relative scale, it is found that higher carbon prices and larger relative scales of carbon markets in pilot regions strengthen the facilitating effects of carbon emissions trading policies on their carbon neutrality progress. Market liquidity, however, only reinforces these policy effects in the dimension of economic development. This study provides empirical evidence and policy recommendations for scientifically measuring the progress towards achieving "dual carbon" targets, improving the national carbon market, and facilitating high-quality economic transformation.
Stock index forecasting is crucial for financial market regulation and investment decision-making. From a fresh perspective of industry risk connectedness, this study proposes a novel multivariate time series graph neural network (MTGNN) model based on risk spillover network. The model uses risk spillover network as the spatial dependency and multiple node features, including sentiment indicator and net risk spillover index as the temporal dependency to predict industry indices. A comprehensive comparison is conducted among the proposed model and five alternatives. Additionally, this study integrates point and interval forecasting results to propose a trading strategy with interval constraints. The study shows that: 1) The MTGNN model based on risk spillover network outperforms traditional machine learning and deep learning methods in forecasting industry stock indices; 2) the investor sentiment indicator and net risk spillover index significantly enhance the prediction performance of the MTGNN model; 3) the interval-constrained trading strategy ensures high returns and greater stability during backtesting. This study offers investors a practical tool for forecasting stock indices and provides decision support for macroprudential regulation.
This paper introduces digital financial capability into the intertemporal decision model, constructs a theoretical analysis framework to explore the impact mechanism of digital financial capability on household wealth accumulation, and conducts an empirical test based on the data of China Household Finance Survey (CHFS). The research shows that digital financial capability can significantly promote household wealth accumulation in China, particularly for rural households and those with low education and low wealth levels. Mechanism analysis shows that increasing financial investment returns and promoting social interaction are two channels through which digital financial capability can improve household wealth accumulation. Further analysis shows that there are structural differences in the impact of digital financial capability on household wealth accumulation, which can improve the allocation of productive assets and financial assets, and reduce the holding of housing assets and other non-financial assets. The above research conclusions provide a new perspective to explain the accumulation of household wealth in China, and also provide a reference for the formulation of relevant policies to promote common prosperity.
Currently, the non-fungible token (NFT) market is experiencing significant price volatility. This study aims to detect the bubble phenomena in the NFT market and analyze the key features and mechanisms affecting NFT price bubbles. Firstly, the study focuses on the NFT market and three important sub-markets, utilizing the Generalized Supremum Augmented Dickey-Fuller (GSADF) test to identify the occurrence, duration, and dissipation of NFT price bubbles. Secondly, traditional financial asset prices, market sentiment indices, and cryptocurrency prices are incorporated as features to analyze NFT price bubbles using multiple decision tree machine learning models. Finally, the SHapley Additive exPlanation (SHAP) method is employed to visualize the mechanisms influencing NFT price bubbles. Empirical results indicate that there were five instances of bubbles in the NFT market during the observation period, with a significant increase in the duration of bubbles across sub-markets in 2021. Among the three machine learning models, the CatBoost (Categorical Boosting) model demonstrated the best performance in fitting NFT price bubbles. SHAP analysis revealed that gold, the US Dollar Index, and crude oil prices significantly impact bubble formation, whereas the S&P 500 has a relatively weak influence. Additionally, market sentiment indices such as the Chicago Board Options Exchange Volatility Index (VIX) and Google Trend show opposite trends in their influence on bubbles. By incorporating multiple features, this study enhances market participants' understanding of NFT price bubble characteristics and provides datadriven market insights.
The operation of insurance funds, a long-term and patient capital, it is crucial for insurance institutional investors to better play their role as social stabilizers and economic shock absorbers and promote corporate green innovation. Using data from China's A-share listed companies from 2013 to 2021, this paper studies and finds that the shareholding of insurance institutional investors has a significantly positive effect on corporate green innovation. A crucial Mechanism explains this positive impact: Insurance institutional investors can motivate companies to carry out green innovation by alleviating the short-termism of management. The results of the heterogeneity analysis find that more field research by insurance institutions and state-owned and large insurance institutional investors can better promote the green innovation of companies. This paper recommends that regulatory authorities continue to improve the insurance fund operation policy and fully encourage insurance funds to carry out green investment. This study provides a scientific basis for China to further promote the development of a low-carbon economy and corporate growth based on the perspective of insurance institutions' shareholding.
The continuous decline of China's manufacturing share has garnered significant attention in recent years, but existing research has yet to reach a consensus on the causes of China's deindustrialization. Given the accelerating integration of manufacturing and services, the impact of the servitization of industrial linkages on the manufacturing share cannot be overlooked. This paper examines this impact from both theoretical and empirical perspectives. Theoretically, based on the production network model, we demonstrate that the servitization of industrial linkages decreases the manufacturing share. Empirically, using a structural decomposition model and time-series input-output data from 1981 to 2018, we decompose the annual changes in China's manufacturing value-added share into the contributions from industrial linkages and five other drivers. The findings reveal that from 1981 to 2006, industrial linkages had a positive impact on the manufacturing value-added share. However, due to servitization, this impact turned negative between 2006 and 2018, making industrial linkages the most significant driver of deindustrialization in China. This discovery complements traditional theoretical explanations of deindustrialization from the perspective of industrial linkages. The paper also explores China's industrial linkage servitization from both time-varying and sectoral perspectives. The role of industrial linkage servitization in China's deindustrialization should be viewed dialectically. On the one hand, the servitization of industrial linkages is a theoretically reasonable trend in light of the information technology revolution and intensified global competition. On the other hand, China's industrial linkage servitization is primarily reflected in the rising proportion of wholesale and retail sectors in the manufacturing input structure, as well as finance and real estate in the services input structure. Therefore, strong promotion of deep and two-way integration of manufacturing and services remains essential to prevent the economy from shifting from a real to a fictitious basis.
Since the incremental input-output model includes positive and negative data and 0, its basic coeffcient concepts and properties are diffcult to explain, coupled with the reasons whether the incremental inverse matrix exists or not, then the theoretical research progresses slowly. This paper, based on the input-output economic theory, analyzes the concept of incremental direct consumption coeffcient, derives an extended formula to give it new connotations, uncovers properties different from those of the general input-output model, proposes the existence forms with five cost and technological change characteristics, and uses mathematical analysis and MATLAB R2018b to primarily verify the existence of the incremental inverse matrix. It defines the concepts of a series of coeffcients such as incremental consumption and distribution coeffcients, influence and sensitivity, then gives the calculation formula. It also proposes the hypothetical nature of the incremental model and the geometric representation of the production function. Finally, based on the incremental consumption and distribution coeffcients, the analysis system of incremental input-output model is preliminarily established, which provides an analytical idea for revealing the characteristics and motivations of technological progress and contribution of each sector in the economic system.
Gold and silver, due to their unique financial properties, have become preferred choices for investment and asset preservation. Accurately quantifying and predicting their price fluctuations is crucial for investors' risk management decisions. This paper introduces a rich set of feature variables and employs a forward rolling algorithm to forecast the realized volatility (RV) of gold and silver futures in Shanghai. We compare the performance of various machine learning models under different loss functions and evaluation methods. The results indicate that the gradient boosting decision tree (GBDT) models demonstrate superior performance in forecasting the futures market for precious metals. Furthermore, this study integrates the XGBoost model with interpretability tools to analyze the dynamic contributions of feature variables to the predicted values in the precious metals futures market. It also assesses the heterogeneous impact of significant variables on predictive performance. Our findings reveal the critical role of market sentiment variables, as well as the relative contributions of macroeconomic variables and volatility decomposition variables under different market conditions. The research provides clear evidence for the selection of factors and models in forecasting precious metal futures market volatility, offering credible investment and management recommendations for investors and regulators in this market.
