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Abstract Online food delivery has become a popular mode of urban food consumption in China as its underlying business mechanism, Online To Offline (O2O), gaining popularity. However, the environmental impacts of a rapidly expanding online food delivery industry and its potential to mitigate environmental burdens remained unexplored in China. Our research found that Chinese cities generated 1.67 MtCO2-equivalent (CO2e) from 13.07 billion times of deliveries in 2019, including transport and packaging. The transportation-related GHG emissions were 745 KtCO2e in 2019, with an average of 0.057 kg CO2e per order and an average of 0.011 kg CO2e per capita. These emissions have surged from 0.31 MtCO2e in 2014 to 2.74 MtCO2e in 2021. We predict that this figure will increase further to 5.94 MtCO2e by 2035. However, with a range of policies such as replacing motorcycles with electric bikes and optimizing traffic routes, it is possible to mitigate such GHG emissions by 4.39–10.97 MtCO2e between 2023 and 2035. These findings highlight the need for further research into the environmental impact of online food delivery and the potential for mitigating it.

Abstract This study investigates the extent and persistence of major crisis events in the crude oil market and economy and searches for general rules of event impact. Although the short-term effects of such crises may quickly become evident, their long-term implications can be challenging to uncover. To this end, we analyzed 50 major crisis events across four categories using a Proxy structural vector autoregressive (Proxy-SVAR) model. The results show that these events substantially impacted crude oil production, world crude oil prices, and inflation in China and the United States. Generally speaking, major crisis events have a more severe impact on the crude oil market, but their impact on the economy lasts longer. Notably, geopolitical conflicts have led to a sharp decline in S&P500, resulting in a simultaneous reduction in the industrial production indices of the world, China, and the US. Among the different types of crises, the financial crisis had the most prolonged impact, persisting for approximately four years. The public health emergency represented by COVID-19 resulted in a decline in actual oil prices, with its impact on the oil market continuing for over three years. Considering our findings, we suggest four policy measures to enhance economic resilience.

Abstract Mining diamond poses significant and potentially underestimated risks to the environment worldwide. Here, we propose a Diamond Environmental Impacts Estimation (DEIE) model to forecast the environmental indicators, including greenhouse gas (GHG) emissions, mineral waste, and water usage of the diamond industry from 2030 to 2100 in the top diamond production countries under different Shared Socio-economic Pathways (SSPs). The DEIE projection results indicate that the annual GHG emissions, mineral waste, and water usage of the global diamond industry will reach 9.65 Mt, 422.80 Mt, and 78.68 million m3 under the SSP1-1.9 scenario, and 13.26 Mt, 582.84 Mt, and 107.95 million m3 under the SSP2-2.6 scenario in 2100, respectively. We analyze the environmental impact heterogeneities and the associated driving factors across the major diamond production countries identified by our DEIE framework. In addition, we find that lab-grown diamonds can reduce annual GHG emissions, mineral waste, and water usage by 9.58 Mt, 421.06 Mt, and 66.70 million m3 in 2100. The lab-grown diamond substitution policy can annually save 714 million cubic meters of landfill space, harvest 255 million kilograms of rice, feed 436 million people, and lift 1.19 million households out of hunger. The lab-grown diamond substitution policy could contribute to the diamond industry’s GHG mitigation and sustainability efforts in a cost-saving manner.

Abstract Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, and pandemic text data using natural language processing, and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices. An interval constraint-based trading strategy is constructed. Using data from several typical stocks in the Chinese stock market during the COVID-19 period, the empirical studies and trading simulations show, first, that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting. Second, the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus, can assist investors in decision-making.

Abstract As the second-largest oil producer and natural gas exporter, Russia’s war with Ukraine has severely impacted the energy market. To what extent has the war influenced crude oil prices, and has it altered the long-term dynamics of oil prices? An objective analysis of the effects of the Russia–Ukraine war on the crude oil market can assist relevant entities in developing both short-term emergency strategies and long-term response plans. This study establishes an analytical framework of the event analysis method based on multiresolution causality testing (EMC). The results of the multiresolution causality testing reveal a significant one-way causality between the Russia–Ukraine war and crude oil prices. Afterward, using the event analysis based on variational mode decomposition (VMD), from October 1, 2021, to August 25, 2022, as the event window, we found that the war and its chain events caused the West Texas Intermediate (WTI) crude oil prices to increase by $37.14, a 52.33% surge, and the Brent crude oil price to rise by $41.49, a 56.33% increase. During the event window, the Russia–Ukraine war can account for 70.72% and 73.62% of the fluctuation in WTI and Brent crude oil prices, respectively. Furthermore, the war amplified oil price volatility and fundamentally altered the trend of crude oil prices. Consequently, this study proposes four recommendations: the establishment of an emergency management mechanism for the oil market, the diversification of oil and gas imports by energy-importing countries, the steady advancement of energy transformation, and the judicious use of financial instruments by enterprises to hedge risks.

Summary: Chinese cities need independent but synergetic dual-carbon abatement roadmaps to mitigate climate change and achieve carbon neutrality. Using source-level data, we develop a time-series, full-scale emission inventory for all Chinese cities from 2005 to 2020, exploring associated heterogeneous and homogeneous patterns. We find that 31% of cities have had a significant carbon emission peak, with the main driver being carbon intensity reductions through efficiency gains and structural improvements. Despite discrepant emission levels and socioeconomic determinants, a uniform trajectory in emission changes exists across cities via four emission phases: growth of 8%–9% annually (95% confidence interval) before peaking; plateau and decline by 9%–13% for 5–7 years; and plain with slower declines. We project that if cities follow their early-peaked counterparts’ mitigation pathways, China will reach a carbon peak in 2026 at 13 Gt and carbon neutrality during 2051–2058, revealing the feasibility of Chinese climate goals and the importance of long-reaching, city-targeted planning. Science for society: China established its dual-carbon goals to achieve a carbon peak before 2030 and carbon neutrality by 2060. It is important for cities to identify their distinctive patterns and define individual dual-carbon roadmaps to achieve carbon neutrality in China. In this study, we conduct a carbon inventory for all Chinese cities from 2005 to 2020 to quantitatively define the emission phases in the process of carbon peak. We find that 31% of cities have had a significant carbon emission peak, with the main driver being carbon intensity reductions. A uniform trajectory in emission changes exists across cities, despite significant differences in emission levels and socioeconomic determinants. We project that if cities follow their early-peaked counterparts’ mitigation pathways, China could achieve its climate change goals ahead of the policy deadlines.

Summary: With the increasing electrification of road transport in China, there is a need to put eyes on the disposal of retired batteries. Reusing the retired batteries for rural electrification presents great promise, but this possibility has not been comprehensively investigated. We study how to contribute to sustainable development goals and carbon neutrality in China by establishing a battery-reusing system in rural residential sector from 2021 to 2060. Our results show that, before 2060, the accumulated rural residential installations of distributed solar photovoltaics with reused batteries can reach 364.2∼403.0 GW. The avoided annual CO2 emissions range from 505.9 to 536.4 million tons, and the total recovered lithium and cobalt from retired batteries will be no less than 88.8% and 60.0% of their respective 2021 global mining volumes. The system is believed to introduce changes to subsidy-driven policy to promote sustainable development by transitioning to one that is market based. Science for society: With increasing electrification of road transport in China, there will be rapid growth in the volume of retired electric vehicle batteries. However, effective systems of collection, recycling, and reuse of retired batteries are lacking. Meanwhile, the removal of subsidies on distributed solar photovoltaics (DSPVs) will slow down its deployment in China’s rural residential sectors. We study how to contribute to sustainable development goals and carbon neutrality in China by establishing a battery-reusing system through matching retired batteries to the deployment of DSPV in rural residential sector from 2021 to 2060. Results show that, before 2060, the accumulated rural residential installations of DSPV with reused batteries can reach 364.2∼403.0 GW, and the avoided annual CO2 emissions range from 505.9 to 536.4 million tons. The solution can be an important lever for China to decarbonize and potentially contributes to multiple sustainable development goals.

Abstract This paper investigates optimal decisions for private banking development in China under two innovative organizational structures—the big retail mode (BRM) and the independent development mode (IDM). Under the BRM, the retail and private banking divisions form a cooperative relationship wherein the former transfers high-net-worth customers to the latter. In addition, retail banking receives a share of private banking revenues. We investigate the optimal revenue-sharing ratio between the two divisions and the optimal effort by private banking to serve transferred customers within the cooperative relationship. The analytical results show that as the private banking division becomes more developed, the optimal revenue-sharing ratio decreases, and the private banking division’s optimal effort to serve transferred customers decreases because it puts more effort into acquiring new customers. Under the IDM, the two divisions form a competitive relationship since they compete to acquire customers independently. We investigate customer acquisition efforts in this interdivisional competition. Optimal customer acquisition efforts by both divisions increase in potential assets and rates of return. This paper contributes to the literature by (1) analyzing financial innovation by private banks from an organizational perspective; (2) providing an economic analysis for private banking development in China.

This study analyzes the performance of the Shanghai Composite Index, S&P 500 index, WTI oil price, and LBMA gold price when wars took place, especially the Russia-Ukraine conflict. We employ empirical methods to explore the stability, instantaneous shock, and short-term shock regarding the abovementioned financial assets. We first adopt the event study method to ascertain whether the cumulative abnormal returns of the selected assets are significant when wars break out. Then, we use the permutation test to examine the significance of price level changes. Results show that only the Shanghai Composite Index is relatively stable. Second, the difference-in-differences model indicates that the 3 unstable assets all suffered positive shocks in their price levels within several days after the Russia-Ukraine conflict broke out. The parallel trend test confirms the validity of establishing the difference-in-differences model. Third, regression discontinuity is designed to measure the impact in a longer event window, suggesting the robustness of conclusions of the difference-in-differences model and revealing an upward trend before the conflict and a downward trend after the conflict of the financial assets. The study suggests that investors consider adjustments to investment strategies and governments take precautions to diminish the risk of the outbreak of wars.

Abstract The transportation sector is a crucial source of greenhouse gas emissions, and the degree of its low-carbon transformation is closely related to the achievement of China’s carbon neutrality. Based on high-frequency passenger vehicle sales data and motor vehicle real-time monitoring big data, we developed a low-carbon transition planning model of China road transport (CRT-LCTP) to explore the pathways toward carbon neutrality. The study found that although the number of new energy vehicles (NEVs) increased four times from 2016 to 2019, the average annual growth rate of road traffic emissions was still as high as 20.5%. The current transportation electrification may only reduce 0.6% of the total emissions in this sector, and it could be increased to 1.4% if the electricity completely came from clean energy. Under the enhanced policy scenario, the transport sector could peak its carbon emissions at around of 2030, with the peak level being 1330.98 Mt. Transportation electrification along could not meet the climate targets in 2060, and the continued inertia of fuel vehicles will slow the path of the road transport toward carbon neutrality, which depends on the forced elimination of fuel vehicles and more substantive decarbonization measures.

Summary: Industry redistribution is a common economic phenomenon that involves a dynamic configuration of the production location across a region, country, or the world. However, measurements of the associated pollutant emission effects have not been well conducted at the domestically regional level. Here, we calculate the CO2 emission changes induced by China’s domestic inter-provincial industry redistribution during 2002–2017 using a counterfactual approach in the multi-regional input-output framework. We find that China’s domestic industry redistribution decreased CO2 emissions during 2002–2017 and has considerable potential to continuously mitigate CO2 emissions in the future. We emphasize that the pollution haven effect may accompany the process of industry redistribution but can be weakened by effective policies, including stringent access thresholds in the regions undertaking industry relocation and regional industry structural upgrading. This paper provides policy recommendations for strengthening regional coordination to achieve China’s transformation to carbon neutrality.

Accurate forecasting results are crucial for increasing energy efficiency and lowering energy consumption in wind energy. Big data and artificial intelligence (AI) have great potential in wind energy forecasting. Although the literature on this subject is extensive, it lacks a comprehensive research status survey. In identifying the evolution rules of big data and AI methods in wind energy forecasting, this paper summarizes the studies on big data and AI in wind energy forecasting over the last two decades. The existing big data types, analysis techniques, and forecasting methods are classified and sorted by combining literature reviews and scientometrics methods. Furthermore, the research trend of wind energy forecasting methods is determined based on big data and artificial intelligence by combing the existing research hotspots and frontier progress. Finally, this paper summarizes existing research’s opportunities, challenges, and implications from various perspectives. The research results serve as a foundation for future research and promote the further development of wind energy forecasting.

Abstract In recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers. However, empirical works in the Bitcoin forecasting and trading support systems are at an early stage. To fill this void, this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two primary steps are involved in our methodology framework, namely, data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price. Results demonstrate that the proposed model outperforms other benchmark models, including econometric models, machine-learning models, and deep-learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals.

The Regional Comprehensive Economic Partnership (RCEP) will come into force in January 2022. Here the authors quantify ex ante economic and environmental effects following RCEP tariff reductions.

Abstract The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention. Taking the Google search volume index as the measure of internet attention, time-varying Granger causality between the global Bitcoin market and internet attention is examined. Empirical results show a strong Granger causal relationship between internet attention and trading volume. Moreover, they indicate, beginning in early 2018, an even stronger impact of trading volume on internet attention, which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble. Although Bitcoin returns are found to strongly affect internet attention, internet attention only occasionally affects Bitcoin returns. Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices, and internet attention is more likely to lead to returns during Bitcoin bubbles. These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.

Abstract In recent years, the crude oil market has entered a new period of development and the core influence factors of crude oil have also been a change. Thus, we develop a new research framework for core influence factors selection and forecasting. Firstly, this paper assesses and selects core influence factors with the elastic-net regularized generalized linear Model (GLMNET), spike-slab lasso method, and Bayesian model average (BMA). Secondly, the new machine learning method long short-term Memory Network (LSTM) is developed for crude oil price forecasting. Then six different forecasting techniques, random walk (RW), autoregressive integrated moving average models (ARMA), elman neural Networks (ENN), ELM Neural Networks (EL), walvet neural networks (WNN) and generalized regression neural network Models (GRNN) were used to forecast the price. Finally, we compare and analyze the different results with root mean squared error (RMSE), mean absolute percentage error (MAPE), directional symmetry (DS). Our empirical results show that the variable selection-LSTM method outperforms the benchmark methods in both level and directional forecasting accuracy.

Abstract The emergence and growing popularity of Bitcoins have attracted the attention of the financial world. However, few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market. It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio. In this study, we propose a novel ensemble portfolio optimization (NEPO) framework utilized for broad commodity assets, which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation. Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors. In addition, it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies, commodity funds, and indices.

The need to make more accurate grain demand (GD) forecasting has become a major topic in the current international grain security discussion. Our research aims to improve short-term GD prediction by establishing a multi-factor model that integrates the key factors: shifts in dietary structures, population size and age structure, urbanization, food waste, and the impact of COVID-19. These factors were not considered simultaneously in previous research. To illustrate the model, we projected China’s annual GDP from 2022 to 2025. We calibrated key parameters such as conversion coefficients from animal foods to feed grain, standard person consumption ratios, and population size using the latest surveys and statistical data that were either out of date or missing in previous research. Results indicate that if the change in diets continued at the rate as observed during 2013–2019 (scenario 1), China’s GD is projected to be 629.35 million tons in 2022 and 658.16 million tons in 2025. However, if diets shift to align with the recommendations in the Dietary Guideline for Chinese Residents 2022 (scenario 2), GD would be lower by 5.9–11.1% annually compared to scenario 1. A reduction in feed grain accounts for 68% of this change. Furthermore, for every 1 percentage point increase in the population adopting a balanced diet, GD would fall by 0.44–0.73 million tons annually during that period. Overlooking changes in the population age structure could lead to an overprediction of annual GDP by 3.8% from 2022 to 2025. With an aging population, China’s GD would fall slightly, and adopting a balanced diet would not lead to an increase in GD but would have positive impacts on human health and the environment. Our sensitivity analysis indicated that reducing food waste, particularly cereal, livestock, and poultry waste, would have significant effects on reducing GD, offsetting the higher demand due to rising urbanization and higher incomes. These results underscore the significance of simultaneous consideration of multiple factors, particularly the dietary structure and demographic composition, resulting in a more accurate prediction of GD. Our findings should be useful for policymakers concerning grain security, health, and environmental protection.

The growing energy consumption and carbon emissions of Bitcoin mining could potentially undermine global sustainability efforts. Here, the authors show the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 Twh and generate 130.50 million metric tons of carbon emissions.

We consider a joint inventory-pricing control problem in a single-product, periodic-review, dual-supplier inventory system. The two suppliers have different lead times. One expedited supplier offers instantaneous replenishment, and one regular supplier requires an L-period lead time for delivery. The supply quantity is stochastic and the demand is price-dependent. For the expedited inventory replenishment, we characterize the optimal policy as a state-dependent almost-threshold policy by extending the stochastically linear in mid-point to a multidimensional setting. To investigate the optimal regular inventory replenishment and pricing policy, we propose the notions of partially stochastic translation (PST) and increasing partially stochastic translation (IPST), which help in obtaining the antimultimodularity preservation in dynamic programming problems. We provide properties, sufficient conditions, and examples for PST and IPST functions. By applying PST and IPST, we obtain the antimultimodularity of the profit functions. The antimultimodular profit functions ensure that the optimal regular ordering quantity and the optimal price are monotone in the current inventory level and outstanding order quantities. Moreover, we reveal that as the time interval increases, the effects of previous outstanding orders on the optimal regular ordering and pricing decisions are decreasing and increasing, respectively. PST and IPST also enable us to further characterize the optimal expedited ordering quantity as decreasing in the inventory level. However, the optimal expedited ordering quantity can be non-monotone with respect to the outstanding order quantities, as shown in the example.

There lacks a comprehensive analysis on the large-scale deployment of solar photovoltaic projects and its impact on poverty alleviation. Here the authors show that solar photovoltaic poverty alleviation pilot policy increases per-capita disposable income in a county by approximately 7%-8%.

Electricity demand forecasting plays a fundamental role in the operation and planning procedures of power systems, and the publications related to electricity demand forecasting have attracted more and more attention in the past few years. To have a better understanding of the knowledge structure in the field of electricity demand forecasting, we applied scientometric methods to analyze the current state and the emerging trends based on the 831 publications from the Web of Science Core Collection during the past 20 years (1999–2018). Employing statistical description analysis, cooperative network analysis, keyword co-occurrence analysis, co-citation analysis, cluster analysis, and emerging trend analysis techniques, this study gives a comprehensive overview of the most critical countries, institutions, journals, authors, and publications in this field, cooperative networks relationships, research hotspots, and emerging trends. The results can provide meaningful guidance and helpful insights for researchers to enhance the understanding of crucial research, emerging trends, and new developments in electricity demand forecasting.

Summary: The shift of China’s economy since 2013, dubbed the “new normal”, has caused its production and consumption emissions to plateau, with the country seeming to embody the tantalizing promise of decoupling its economic growth from carbon emissions. By using multi-region input-output analysis, we find that China’s relative decoupling in the new normal is technology driven, evidenced by the narrowing gap between its technology-adjusted and non-adjusted consumption emissions. By applying structural decomposition analysis, we further explore the driving forces behind the slowdown in China’s imported emissions growth, finding that it is attributable to restructuring of import patterns resulting from changes in the structures of domestic demand. These changes could have been caused by China moving along the global value chain and rebalancing its industrial linkages toward trade in carbon-efficient goods to avoid transferring emissions-intensive production to other regions, indicating a shift to less emissions-intensive trade rather than pure outsourcing.

The development of big data generation, acquisition, storage, processing, and other technologies has greatly enriched our sensory world and fundamentally changed the basis of traditional economic and financial forecasting. Unexpected events in the economic and financial fields challenge our confidence in the performance of forecasting models. Obviously, the big data-driven decision theories and analysis methods are different from the traditional methods. In view of the important role of big data-driven economic and financial forecasting in social stability, innovative development, and sustainability, the research frontiers of big data-driven economic and financial forecasting in the future include: feature mining of complex economic systems with big data representation; accurate real-time correction of theories and methods of dynamic forecasting and early warning; general paradigm of big data forecasting research; formation and process of big data-driven economic and financial system management mechanism, etc. Systematic research on such issues will contribute to the formation of decision-making theories and research systems in the context of big data, thus improving the adaptability and scientificity of management decisions.

Abstract The COVID‐19 outbreak has been one of the world's most severe challenges since World War II, which not only reflected in the observable economic depression, but also in emotional damage that is scarcely underscored. This paper estimates the effect of the epidemic on happiness of Chinese residents by capturing the emotions conveyed through massive social media, combined with city‐level epidemic data. We confirm the significant negative impact of the outbreak on life satisfaction, with an extra 1% of cumulative death cases associated with a 0.54% decrease in expressed happiness. Meanwhile, we find that well‐being drops the most in regions with high‐level risk of COVID‐19, and happiness in regions with higher or lower income levels are more vulnerable to the effects of the epidemic, in comparison to the intermediate‐level income regions. Female sentiment exhibits more sensitivity to the epidemic situation than does male sentiment. Furthermore, the timing of major public events also plays a sizable role in determining self‐reported happiness, and our results suggest that pandemic alerts announced by the World Health Organization significantly increased the loss of well‐being, while announcements of city‐unlocking had the opposite impact. The interaction between the outbreak and real‐time expressed happiness provides an important opportunity for reflection on the future governance policies of epidemic containment.

In view of the intrinsic complexity of the oil market, crude oil prices are influenced by numerous factors that make forecasting very difficult. Recognizing this challenge, numerous approaches have been introduced, but little work has been done concerning the interval-valued prices. To capture the underlying characteristics of crude oil price movements, this paper proposes a two-stage forecasting procedure to forecast interval-valued time series, which generalizes point-valued forecasts to incorporate uncertainty and variability. The empirical results show that our proposed approach significantly outperforms all the benchmark models in terms of both forecasting accuracy and robustness analysis. These results can provide references for decision-makers to understand the trends of crude oil prices and improve the efficiency of economic activities.

A decomposition clustering ensemble (DCE) learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition (VMD), the self-organizing map (SOM) network, and the kernel extreme learning machine (KELM). First, the exchange rate time series is decomposed into N subcomponents by the VMD method. Second, each subcomponent series is modeled by the KELM. Third, the SOM neural network is introduced to cluster the subcomponent forecasting results of the in-sample dataset to obtain cluster centers. Finally, each cluster's ensemble weight is estimated by another KELM, and the final forecasting results are obtained by the corresponding clusters' ensemble weights. The empirical results illustrate that our proposed DCE learning approach can significantly improve forecasting performance, and statistically outperform some other benchmark models in directional and level forecasting accuracy. Keywords: Exchange rates forecasting, Variational mode decomposition, Kernel extreme learning machine, Self-organizing map, Decomposition ensemble learning

Macroeconomic forecasting in China is essential for the government to take proper policy decisions on government expenditure and money supply, among other matters. The existing literature on forecasting Chinas macroeconomic variables is unclear on the crucial issue of how to choose an optimal window to estimate parameters with rolling out-of-sample forecasts. This study fills this gap in forecasting economic growth and inflation in China, by using the rolling weighted least squares (WLS) with the practically feasible cross-validation (CV) procedure of Hong et al. (2018) to choose an optimal estimation window. We undertake an empirical analysis of monthly data on up to 30 candidate indicators (mainly asset prices) for a span of 17 years (2000–2017). It is documented that the forecasting performance of rolling estimation is sensitive to the selection of rolling windows. The empirical analysis shows that the rolling WLS with the CV-based rolling window outperforms other rolling methods on univariate regressions in most cases. One possible explanation for this is that these macroeconomic variables often suffer from structural changes due to changes in institutional reforms, policies, crises, and other factors. Furthermore, we find that, in most cases, asset prices are key variables for forecasting macroeconomic variables, especially output growth rate. Keywords: Cross-validation, Optimal rolling window, Rolling out-of-sample forecasts, Structural changes, Weighted least squares, JEL classification codes: C2, C13

Abstract By decomposing asset returns into potential maximum gain (PMG) and potential maximum loss (PML) with price extremes, this study empirically investigated the relationships between PMG and PML. We found significant asymmetry between PMG and PML. PML significantly contributed to forecasting PMG but not vice versa. We further explored the power of this asymmetry for predicting asset returns and found it could significantly improve asset return predictability in both in-sample and out-of-sample forecasting. Investors who incorporate this asymmetry into their investment decisions can get substantial utility gains. This asymmetry remains significant even when controlling for macroeconomic variables, technical indicators, market sentiment, and skewness. Moreover, this asymmetry was found to be quite general across different countries.

This article innovatively builds the infrastructure of farmer credit rating index system into a multilevel unidirectional network structure. First, according to the logical structure of the three-level credit rating index system, a four-level unidirectional network is constructed, and the credit rating calculation formulas of all indexes at the four-level network are established. Furthermore, the special cases of the credit rating formula with the first- and second-level farmer credit rating index system are discussed. On this basis, it is extended to a credit rating index system with more than four levels, and the corresponding credit rating formula is established. Finally, the general formula of credit rating formula of the farmer credit rating index system from first level to multilevel is obtained. In order to solve the problem of farmers' credit rating, this paper also designs a linear segmentation classifier to classify the results of multilayer unidirectional network, establishes the rules of farmers' credit rating and the unidirectional network linear segmentation evaluation model of farmers' credit rating, and discusses the properties of bank credit based on farmers’ credit rating. Finally, the model established in this paper is applied to the credit rating of farmers in A County, Guangdong Province in China. When the credit rating of 160 farmers is carried out, the evaluation results are in line with the actual credit rating of farmers in A County, with an accuracy of 100%. This research has the maneuverability to carry on the scientific credit rating to the countryside. This study has important method guidance and operability for rural credit rating.

In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today’s world economic activities, named a “barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008–2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets.

This study conducted a questionnaire-led survey to explore the financial feasibility and socio-environmental impacts of stand-alone solar home systems (SHS) through stratified random sampling. Based on the above consideration, fifteen cases of studies of various watt peak (Wp) capacities have been investigated to evaluate the economic viability of solar home systems. The results revealed that most of the cases have positive net present value (NPV) and low payback periods, with an internal rate of return (IRR) value ranging from 16% to 131%, which signifies a high rate of investment exchange. Solar home systems are economically profitable for micro-enterprises and households with low-income generation activities as opposed to the households using it only for lighting. The study found that solar home systems with a capacity above 30 Wp are the most economically viable option, which can also avoid 6.15 to 7.34 tonnes of CO2 emissions during the 20 years of life-cycle, while providing different applications including lighting, recreation, information, health, and economic benefits.

In this article, the relationship between energy consumption and economic growth is examined from the viewpoint of China’s industrial sectors. Panel data from 37 industrial sectors in China covering the period from 1998 to 2010 was used in this study. Not only first generation panel unit root tests and panel cointegration tests, but also second generation tests that account for dependence between cross-sectional units were employed. The empirical results reveal that both energy consumption and economic growth are integrated as order one, and they are cointegrated. Panel fully modified ordinary least squares estimators show that a 1% increase in energy consumption increases the real value added of industrial sectors by 0.871%, and a 1% increase in real value added of industrial sectors increases energy consumption by 1.103%. The panel vector error correction models for causality tests are estimated by a system generalized moment method. We find a unidirectional causal relation running from economic growth to energy consumption in the shortrun. In the long run, however, there is evidence of a unidirectional causality running from energy consumption to economic growth.

Significant differences in key results across the various climate models and integrated assessment models (IAMs) represent a critical challenge to reliable scientific findings and the robust design of climate policies, which leads to an enormous amount of attention and the urgent call for a multi-model study. In this paper, we develop an integrated literature–survey framework by combining the typical content analysis with a simple statistical analysis to systematically examine the developing trends of IAM-based multi-model studies and explore the model-robust climate policy findings; we also conduct an extended analysis to identify the role of a multi-model approach in global warming and other global change research by employing co-citation network analysis. The results reveal that multi-model comparison and ensemble are effective methods to explore reliable scientific findings and yield robust policy conclusions. The current multi-model studies are sparse as a whole, especially for IAM-based climate economic and policy research; future multi-model works, at both the global and regional levels, are therefore promising. We observe that the developed countries (the EU and the US) dominate the current multi-model study, which could be proved by the number of primary IAMs developed, frequency of models adopted, and number of works published. Addressing the risks of global warming relies on reliable scientific research and robust climate policy design, particularly for the developing large emitters, which heavily depends on consistent efforts toward primary model development and comprehensive cooperation with state-of-the-art model teams all over the world.

Since natural gas has become a new star in China’s energy mix, a reliable estimation of the price elasticity of natural gas demand is crucial if we are to understand how energy price changes affect natural gas consumption. In this paper, we conduct a Meta-regression analysis to quantitatively synthesize empirical estimates of the price elasticity of natural gas demand reported in previous studies, provide true underlying values, and explain the heterogeneity of the aforementioned estimates. The Fixed-effects model and ordinary least squares (OLS) are applied to estimate the regression models. As a result, this paper reports a mean elasticity of −1.521 and 0.410 for the short- and long-run own-price elasticity, separately; −0.762 and 0.008 for the short- and long-run cross-price elasticity-coal to natural gas, respectively; 2.122 and 1.884 for the short- and long-run cross-price elasticity-electricity to natural gas, separately; and 2.267 and 1.275 for the short- and long-run cross-price elasticity-oil to natural gas, respectively. Our results suggest that natural gas consumption increases with the decrease of its own and coal prices in the short term and rise of electricity and oil prices. It also shows that almost all heterogeneity can be explained by the type of data, sample period, models of analysis, geographical region, and type of consumer.

In this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight) in aggregate (TPA, TFA), and civilian vehicle number (CVN) and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015) based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA) and others is made to assess our task.

This study investigated the impact of banking management on credit risk using a sample of Indian commercial banks. The study employed dynamic panel estimations to evaluate the link between banking management variables and credit risk. The empirical results show that an increase in loan portion over total assets does not necessarily increase problem loans. The findings suggest that high capital requirements and large bank size do not reduce default risk, whereas high profitability and strong income diversification policies lower the likelihood of default risk. The overall empirical results supported the “operating efficiency”, “diversification” and “too big to fail” hypotheses, confirming that credit quality in the banking industry is mainly driven by profitability, banking supervision, high credit standards and strong investment strategies. The findings are relevant to bank managers, investors and bank regulators, in formulating effective credit policies and investment strategies.

Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

Considering the speedy growth of industrialization and urbanization in China and the continued rise of coal consumption, this paper identifies factors that have impacted coal consumption in 1985–2011. After extracting the core factors, the Bayesian vector autoregressive forecast model is constructed, with variables that include coal consumption, the gross value of industrial output, and the downstream industry output (cement, crude steel, and thermal power). The impulse response function and variance decomposition are applied to portray the dynamic correlations between coal consumption and economic variables. Then for analyzing structural changes of coal consumption, the exponential smoothing model is also established, based on division of seven sectors. The results show that the structure of coal consumption underwent significant changes during the past 30 years. Consumption of both household sector and transport, storage, and post sectors continues to decline; consumption of wholesale and retail trade and hotels and catering services sectors presents a fluctuating and improving trend; and consumption of industry sector is still high. The gross value of industrial output and the downstream industry output have been promoting coal consumption growth for a long time. In 2015 and 2020, total coal demand is expected to reach 2746.27 and 4041.68 million tons of standard coal in China.

We first analyze quality and price decisions in a supply chain with two Stackelberg games: Manufacturer’s Stackelberg (MS) and Supplier’s Stackelberg (SS). Then, we investigate how equilibrium solutions are influenced by proposed quality improvement policies: coordination and manufacturer’s involvement. Also, we derive the conditions under which the policies can be implemented in both MS and SS strategies. Numerical experiments illustrate the problems and several related issues are discussed. The results suggest that proposed quality improvement policies can realize Pareto improvement for the supply chain performance.

We formulate a pair of symmetric dual nondifferentiable multiobjective programming and establish appropriate duality theorems. We also show that differentiable and nondifferentiable analogues of several pairs of symmetric dual problems can be obtained as special cases of our general symmetric programs.

Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.