Oil price shocks and their short- and long-term effects on the Chinese economy

A considerable body of economic literature shows the adverse economic impacts of oil-price shocks for the developed economies. However, there has been a lack of empirical study of this kind on China and other developing countries. This paper attempts to fill this gap by answering how and to what extent oil-price shocks impact China’s economy, emphasizing on the price transmission mechanisms. To that end, we develop a structural vector auto-regressive model. Our results show that an oil-price increase negatively affects output and investment, but positively affects inflation rate and interest rate. However, with the differentiated price control policies for materials and intermediates on the one hand and final products on the other hand in China, the impact on real economy, represented by real output and real investment, lasts much longer than that to price/monetary variables. Our decomposition results also show that the short-term impact, namely output decrease induced by the cut of capacity-utilization rate, is greater in the first one to two years, but the portion of the long-term impact, defined as the impact realized through an investment change, increases steadily and exceeds that of short-term impact at the end of the second year. Afterwards, the long-term impact dominates, and maintains for quite some time.Structural vector auto-regressive model; Unit root test; Error-correction model; Oil-price shocks; Price transmission mechanisms; Investment; Output; Producer/consumer price index; Census X-12 approach; China

Undermined climate policies : a study on the impact of regulatory and financial discrimination across heterogeneous firms in China

Firms in China within the same industry but with different ownership and size have very different production functions and can face very different emission regulations and financial conditions. This fact has largely been ignored in most of the existing literature on climate change. Using a newly augmented Chinese input–output table in which information about firm size and ownership are explicitly reported, this paper employs a dynamic computable general equilibrium (CGE) model to analyze the impact of alternative climate policy designs with respect to regulation and financial conditions on heterogeneous firms. The simulation results indicate that with a business-as-usual regulatory structure, the effectiveness and economic efficiency of climate policies is significantly undermined. Expanding regulation to cover additional firms has a first-order effect of improving efficiency. However, over-investment in energy technologies in certain firms may decrease the overall efficiency of investments and dampen long-term economic growth by competing with other fixed-capital investments for financial resources. Therefore, a market-oriented arrangement for sharing emission reduction burden and a mechanism for allocating green investment is crucial for China to achieve a more ambitious emission target in the long run

Sensitivity analysis of distributed photovoltaic system capacity estimation based on artificial neural network

Residential solar photovoltaic (PV) system installations are expected to continue increasing due to their growing cost competitiveness and supportive government policies. However, excessive installations of unknown behind-the-meter solar panels present a challenge for accurate load prediction and reliable operations of power networks. To address such growing concerns of distribution network operators (DNOs), this research proposes a novel model for distributed PV system capacity estimations. Innovative extracted features from 24-hour substation net load curves were fed into a deep neural network to estimate the PV capacity linked to the substation feeder. A comprehensive study into the sensitivity of the model’s accuracy to specific temporal scales of data collection, number of households served by a substation, and proportion of PV-equipped properties was conducted. This study revealed that a model developed to be used exclusively in summer achieved a 18.1% decrease in estimation root mean squared error (RMSE) compared to an all-year model, whilst using only a third of the training data amount. Similarly, compared to an all-year model, RMSE decreased by 26.9% when only data from Mondays to Thursdays were used to train and test the model. Also, for the all-year model, the most accurate estimations occur when 20% to 80% of households have PV systems installed and estimation percentage error tend to remain constant at around 10% when more than 20% of households have PV systems installed. A machine learning-ready dataset of substations with known PV capacity and experiment results are both useful to inform DNOs on the potential of the proposed method in reducing grid operation costs

Missing Data Imputation with Graph Laplacian Pyramid Network

Data imputation is a prevalent and important task due to the ubiquitousness of missing data. Many efforts try to first draft a completed data and second refine to derive the imputation results, or "draft-then-refine" for short. In this work, we analyze this widespread practice from the perspective of Dirichlet energy. We find that a rudimentary "draft" imputation will decrease the Dirichlet energy, thus an energy-maintenance "refine" step is in need to recover the overall energy. Since existing "refine" methods such as Graph Convolutional Network (GCN) tend to cause further energy decline, in this work, we propose a novel framework called Graph Laplacian Pyramid Network (GLPN) to preserve Dirichlet energy and improve imputation performance. GLPN consists of a U-shaped autoencoder and residual networks to capture global and local detailed information respectively. By extensive experiments on several real-world datasets, GLPN shows superior performance over state-of-the-art methods under three different missing mechanisms. Our source code is available at https://github.com/liguanlue/GLPN.Comment: 12 pages, 5 figure

“Lock-in” Effect of Emission Standard and Its Impact on the Choice of Market Based Instruments

A country’s existing emission standard policy will lead to a “lock in” effect. When the country plans to adopt new market-based instruments to control greenhouse gas emissions, it must consider this effect as it chooses among instruments to avoid larger efficiency loss. In this paper, we find that the “lock in” effect will cause a kink point to occur on the marginal abatement cost (MAC) curve. This change of shape for the MAC curve reminds us to be cautious in choosing market-based instruments when applying Weitzman’s rule. We also introduce this concept into a dynamic multi-regional computable general equilibrium (CGE) model for China and simulate MAC curves for all regions. After applying Weitzman’s rule, we propose a timeline for introducing price instruments under different marginal benefit (MB) curve scenarios

Comparative study of the discrete velocity and the moment method for rarefied gas flows

In the study of rarefied gas dynamics, the discrete velocity method (DVM) has been widely employed to solve the gas kinetic equations. However, it is usually computationally expensive in dealing with complex geometry and high Mach number flows. In the present work, both classical third-order time implicit DVM and the moment method are employed in finding steady-state solutions of the force-driven Poiseuille flow and flow past a circle cylinder. Their performance, in terms of accuracy, has been compared and analyzed. Choosing the velocity distribution functions (VDFs) obtained from DVM solutions as the benchmark, we reconstruct the expanded VDFs using regularized 13 moment equations (R13) and regularized 26 moment equations (R26) and then compare the accuracy of the expanded VDFs with different order of Hermite polynomial expansions. From the computed velocity profiles and reconstructed VDFs, we have found that the moment method can extend the macroscopic equations into the early transition regime, and the R26 can relatively accurately represent the characteristics of the VDFs in comparison with the gas kinetic model. Conversely, the Navier-Stokes-Fourier (NSF) equations are not able to produce an accurate description of the expanded distribution functions

Decoding the influence of bacterial community structure and algicidal bacteria in a Karenia longicanalis bloom event

IntroductionHarmful algal blooms (HABs) have been increasing in frequency and expanding their ranges on coastlines worldwide in recent decades. Algicidal bacteria play a pivotal role in eliminating HABs, yet the characteristics of bacterial communities and their algicidal activity during a Karenia longicanalis bloom remain poorly understood.MethodsIn this study, we investigated bacterial communities using 16S rRNA sequencing during a K. longicanalis bloom to identify bacteria with high algicidal activity that could be isolated. Five sampling sites in Tongxin Bay, located in Lianjiang County, China, including TX1 to TX5, were selected based on the concentration of K. longicanalis cells.ResultsOur 16S rRNA sequencing results revealed that the TX4 site was enriched with genera known to contain algicidal bacteria, such as Pseudoalteromonas and Alteromonas, which are members of the Gammaproteobacteria class, while Sulfitobacter, a member of the Alphaproteobacteria class, was enriched in the TX5 site. Among the 100 cultivable bacteria isolated from the 5 sampling sites, 6 exhibited an algicidal rate of over 80%, with FDHY-MQ5, isolated from the TX4 site, exhibiting an algicidal rate of approximately 100% against Karenia mikimotoi after 48 hours of challenge with 2% (v/v) bacterial volume (OD600=4.5) concentration. Our 16S rRNA sequencing result showed FDHY-MQ5 was a member of the Pseudoalteromonas genus, and this bacterium also demonstrated high algicidal activity against Heterosigma akashiwo and Alexandrium tamarense.DiscussionOur findings shed light on the changes in bacterial community structure and the algicidal behavior of bacteria towards algae during a K. longicanalis bloom, providing a research basis for a better understanding of HAB management

A 3D study on the amplification of regional haze and particle growth by local emissions

The role of new particle formation (NPF) events and their contribution to haze formation through subsequent growth in polluted megacities is still controversial. To improve the understanding of the sources, meteorological conditions, and chemistry behind air pollution, we performed simultaneous measurements of aerosol composition and particle number size distributions at ground level and at 260 m in central Beijing, China, during a total of 4 months in 2015-2017. Our measurements show a pronounced decoupling of gas-to-particle conversion between the two heights, leading to different haze processes in terms of particle size distributions and chemical compositions. The development of haze was initiated by the growth of freshly formed particles at both heights, whereas the more severe haze at ground level was connected directly to local primary particles and gaseous precursors leading to higher particle growth rates. The particle growth creates a feedback loop, in which a further development of haze increases the atmospheric stability, which in turn strengthens the persisting apparent decoupling between the two heights and increases the severity of haze at ground level. Moreover, we complemented our field observations with model analyses, which suggest that the growth of NPF-originated particles accounted up to similar to 60% of the accumulation mode particles in the Beijing-Tianjin-Hebei area during haze conditions. The results suggest that a reduction in anthropogenic gaseous precursors, suppressing particle growth, is a critical step for alleviating haze although the number concentration of freshly formed particles (3-40 nm) via NPF does not reduce after emission controls.Peer reviewe

A heterozygous moth genome provides insights into herbivory and detoxification

How an insect evolves to become a successful herbivore is of profound biological and practical importance. Herbivores are often adapted to feed on a specific group of evolutionarily and biochemically related host plants1, but the genetic and molecular bases for adaptation to plant defense compounds remain poorly understood2. We report the first whole-genome sequence of a basal lepidopteran species, Plutella xylostella, which contains 18,071 protein-coding and 1,412 unique genes with an expansion of gene families associated with perception and the detoxification of plant defense compounds. A recent expansion of retrotransposons near detoxification-related genes and a wider system used in the metabolism of plant defense compounds are shown to also be involved in the development of insecticide resistance. This work shows the genetic and molecular bases for the evolutionary success of this worldwide herbivore and offers wider insights into insect adaptation to plant feeding, as well as opening avenues for more sustainable pest management.Minsheng You … Simon W Baxter … et al

Recent advances in hydrothermal carbonisation:from tailored carbon materials and biochemicals to applications and bioenergy

Introduced in the literature in 1913 by Bergius, who at the time was studying biomass coalification, hydrothermal carbonisation, as many other technologies based on renewables, was forgotten during the "industrial revolution". It was rediscovered back in 2005, on the one hand, to follow the trend set by Bergius of biomass to coal conversion for decentralised energy generation, and on the other hand as a novel green method to prepare advanced carbon materials and chemicals from biomass in water, at mild temperature, for energy storage and conversion and environmental protection. In this review, we will present an overview on the latest trends in hydrothermal carbonisation including biomass to bioenergy conversion, upgrading of hydrothermal carbons to fuels over heterogeneous catalysts, advanced carbon materials and their applications in batteries, electrocatalysis and heterogeneous catalysis and finally an analysis of the chemicals in the liquid phase as well as a new family of fluorescent nanomaterials formed at the interface between the liquid and solid phases, known as hydrothermal carbon nanodots