Huidan Xue

The global food market’s escalating volatility has led to a complex network of uncertainty and risk transmission across different grain markets. This study utilizes the Time-Varying Parameter Vector Autoregression (TVP-VAR)-Connectedness approach to analyze the price transmission and volatility dynamics of key grains, including wheat, maize, rice, barley, peanut, soybean, and soybean meal, and their dynamic spillover directions, intensity, and network. By integrating the TVP-VAR-Connectedness model, this research captures the time-varying variability and interconnected nature of global grain price movements. The main findings reveal significant spillover effects, particularly in corn prices, with prices of soybean dominating other grains while prices of peanut and corn experience higher external spillover effects from other grains. The conclusions drawn underscore the imperative for policymakers to consider a holistic perspective of all types of grains when addressing global food security, with this study providing valuable insights for risk management in the grain sector at both global level and country level.

The COVID-19 pandemic has drastically challenged urban food systems, has hurt the resilience and fundamental function of urban food systems and also accelerated the trends of digitization and changing preferences of consumers in cities. This research conducted a qualitative analysis of the discourses, actions and interactions of different actors in the urban food systems in China during COVID-19 using an actor-oriented approach and discourse analysis. This research finds that stricter regulations and policies have been implemented by governments to regulate the food supply chain and ensure human health. Local community service personnel, volunteers, stakeholders along the food supply chain and consumers formulated collective actions during the pandemic yet chaos and discourse distortions also emerged at different stages. The pandemic is a preamble to changes in consumers’ preferences and food supply chains in urban communities. There were significant structural changes and a dual structure of urban and rural food systems, where unbalanced supply and demand existed. Collective actions with community governance and an innovative food business model to digitize flows and easily adapt to shocks in food systems are required.

This study aims to investigate the impact of ICT capital services on economic growth and energy efficiency in China at both national and industrial levels during the period 2000–2020. To achieve this aim, this study introduces a measurement method for capital services, explores ICT’s contributions to economic growth, and analyzes the impact of ICT on energy efficiency. The empirical results of this study indicate that although the ICT capital services scale is relatively small, accounting for only 8.87% of the total in 2020, its growth rate is faster than that of non-ICT capital services, and the distribution of ICT capital services varies widely among different industries. Additionally, based on the economic growth decomposition framework, this study finds that the contribution of ICT capital services to economic growth is 6.95% on average. It is significantly higher in certain industries, such as Financial industry; Information transmission, software and information technology services; Construction; and Manufacturing compared to others. The total factor energy efficiency (TFEE) reveals that industries with higher energy consumption have lower energy efficiency, while the panel regression model illustrates that the development of ICT has a positive impact on improving energy efficiency, with variability across industries. Overall, the findings of this study provide crucial scientific evidence and policy implications for promoting the development of ICT and integrating it with various industries, which can significantly contribute to boosting economic growth and energy efficiency.

Factor allocation has an important impact on production efficiency, and this study discusses factor misallocation and proposes an optimized method that could improve efficiency and reduce factor inputs. Under a multi-industry competitive equilibrium model, by introducing distortion tax that represents frictions on factor inputs (capital, labor, energy, and other intermediate consumption), we define factor price distortion indexes to measure factor misallocation and set the standardized comprehensive distortion index at the optimal value of one to obtain optimized allocation of each factor. Using actual and optimized inputs, we compute input-oriented efficiencies separately by employing a slack-based measurement data envelopment analysis (SBM-DEA). The superiority of the new allocation method is tested by comparing changes in efficiency and input redundancy before and after optimization. An empirical test based on China’s manufacturing sector during 1999–2016 shows that, under the optimized reallocation, on average, efficiency is improved by 43.40%, capital, labor, energy, and other intermediate consumption are reduced by 18.06%, 16.34%, 30.91%, and 31.24%, respectively, and the total carbon emission is reduced by 55.22% from 2000 to 2016. Our results imply that factor misallocation causes losses in efficiency and excessive input of factors, and factor allocation needs to be further optimized for sustainable development.

Trade facilitation is essential to lowering overall trade costs and increasing economic returns on both the multilateral and regional levels. Trade facilitation is also an integral part of trade agreements and has become an important component of the Belt and Road Initiative (BRI). This paper adopts a time-varying stochastic frontier gravity model and a trade inefficiency model and assigns values to different trade facilitation provisions to construct relevant indicators to assess the impacts of the trade facilitation provisions of the World Trade Organization Trade Facilitation Agreement (WTO TFA) and regional trade agreements (RTAs) on trade efficiency between China and BRI countries, as well as on the current conditions and challenges of fresh agricultural product trade between China and countries of the BRI, using data from 2011 to 2020. The results show that the size of domestic markets and population positively affect agricultural trade between China and BRI countries, while geographical distance has a negative impact on their scale of trade. Moreover, this study finds that the obligations set out in trade facilitation provisions of RTAs play an important role in improving trade efficiency, and among these trade facilitation obligations improving the legal provisions of “information transparency”, “customs cooperation” and “advance ruling” can significantly improve trade efficiency, however, some measures relating to “formalities” could potentially constitute barriers to trade. This study innovatively carried out an analysis of the impact of trade facilitation provisions on agricultural trade flows and efficiency from the perspectives of international trade law and economics and provides new policy recommendations for BRI countries to support agricultural trade.

The sustainable economic development of the Yangtze River Economic Belt is a significant part of China’s regional development strategy. The article selects panel data from 11 provinces of the region from 2004 to 2020 and constructs a spatial economic model and a threshold effect model to investigate the impact of energy development and technological innovation on the real economy of this region. The result indicates that technological innovation plays a significantly beneficial role in supporting the development of the local real economy, while its spatial spillover effect to neighboring provinces is not significant. Energy development has a significant negative impact on both the local real economy and that of the neighboring provinces. Such impact is shaped by the threshold effect of the level of technological innovation.

The dairy sector in the European Union (EU) has experienced policy changes and market shocks recently. Using the global vector autoregressive (GVAR) model, this paper explores regional market integration, the feedback between market shocks and price dynamics, and the link between EU’s cheese export markets and energy market. This paper assesses and compares which influencing factors are typically associated with intra-EU and extra-EU cheese export price movement with regards to shocks to crude oil price, farm-gate raw milk price, and consumer price index (CPI) for food and cheese production of six representative EU member states, respectively. Using generalized impulse response functions, this paper finds that EU’s internal cheese export market is not well integrated, while EU’s external market is well integrated, with France as an exception. It also finds that the external cheese export market is vulnerable to shocks from the energy market compared to the internal market. Raw milk prices from the upstream supply chain have strong spill-over effects on EU’s internal cheese export market, yet their impact on extra-EU cheese export prices is relatively less significant. The movement patterns of extra-EU cheese export prices of Ireland and the UK show similar patterns in the long run. It is concluded that the dynamics of cheese export prices in the internal and external markets of the EU are different under market shocks.

Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.

A fast-changing global landscape highlights the importance of understanding spatial price dynamics in key international markets such as China, especially in the era of COVID-19 pandemic with international food trade and food system experiencing an unprecedented challenge. Nowadays, New Zealand’s dominant position in China’s dairy import market is being challenged by European Union (EU) dairy exporters leading to intensified market competition. Using monthly export data of skim milk powder (SMP), we applied threshold cointegration models along with asymmetric error correction models to examine spatial price dynamics and price transmissions of New Zealand and Ireland in Chinese and global markets. We found that New Zealand’s export prices retain their leadership position in China, Ireland’s export prices are well more aligned with those in international markets. In terms of own-country price transmission, Ireland’s relatively symmetric and swift adjustments were found to contrast with New Zealand’s SMP export prices, which displayed more asymmetric price transmissions.