A hybrid framework for carbon trading price forecasting: The role of multiple influence factor

Zhan

et al. 2022

Biochar

Biochar amendment and substituting chemical fertilizers with organic manure (organic substitution) have been widely reported to increase crop production and decrease reactive nitrogen (Nr) loss including nitrous oxide (N2O), nitric oxide (NO), and ammonia (NH3) emissions, and N runoff and leaching. However, few comprehensive evaluations have been performed on the environmental and economic aspects of biochar amendment or organic substitution. Here, we studied the comprehensive effects of biochar amendment, organic substitution, and biochar amendment combined with organic substitution on crop production, Nr loss, and net ecosystem economic benefit (NEEB) in intensive vegetable production by integrating life-cycle assessment for Nr footprints, empirical models for NH3 volatilization and N runoff and leaching derived from peer-reviewed publications and validated by the current measurements and direct field measurement for N2O and NO emissions during 5 consecutive years of vegetable crop rotations. Five fertilization treatments were applied (SN: synthetic fertilizer application; SNB: SN plus 20 t ha−1 biochar amendment; SNM: substituting 50% of chemical N fertilizer with organic manure; SNMB: SNM plus 20 t ha−1 biochar amendment; and CK: no fertilizer or biochar addition). Compared with the SN, the SNB increased vegetable yield (28.4%, p < 0.05; interannually varying from − 10 to 74.9%) and nitrogen use efficiency (29.2%, interannually varying from − 39.7 to 150.4%), and decreased field Nr loss (45.4%, p < 0.01; interannually varying from − 40.3 to 78.4%), and thus improved NEEB by 7.1%; meanwhile, the SNM increased vegetable yield (11.6%, interannually varying from − 5.4 to 27.1%) and nitrogen use efficiency (45.7%, p < 0.05; interannually varying from 2.3 to 154%), reduced field Nr loss (34.9%, p < 0.01; interannually varying from 8.4–39.0%), and thus improved NEEB by 17.8% (p < 0.05) compared to the SN, being 56.0 × 103 Chinese Yuan (CNY) ha−1 crop−1. Due to the high foreground Nr loss during organic manure production and high input costs of biochar production, the SNMB decreased the NEEB by 8.0% as compared to the SN. Moreover, the SNB and SNM improved vegetable qualities by increasing protein, soluble sugar, and vitamin C contents while decreasing nitrate content (p < 0.05). Therefore, single application of biochar amendment or organic substitution would achieve better NEEB and product quality in vegetable production. Graphical Abstract

Section: Uncertainty Analysismentioning

confidence: 99%

Biochar and organic substitution improved net ecosystem economic benefit in intensive vegetable production

Zhan

et al. 2022

Biochar

“…The factors affecting China's carbon price in previous studies mainly include coal, natural gas, crude oil, macroeconomy and international carbon price (Han et al, 2019 ; Hao & Tian, 2020 ; Zeng et al, 2017 ). Based on theoretical analysis and China's actual situation, this study selected eight factors that might affect China’s carbon price from the following three perspectives.…”

Section: Empirical Studiesmentioning

confidence: 99%

“…International markets (especially the crude oil market), have a direct or indirect impact on the production activities of all countries. We considered the potential influencing factors of this impact (including energy markets, the economic situation of China and international carbon markets), and detected a high-dimensional dependence between them and Chinese carbon markets (Hao & Tian, 2020 ; Zeng et al, 2021 ; Zhou & Li, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

High-dimensional nonlinear dependence and risk spillovers analysis between China’s carbon market and its major influence factors

Tian

et al. 2022

Ann Oper Res

In July 2021, China began its national emissions trading scheme, marking a new stage of development for the country’s carbon market. This study analyzes the multidimensional correlation between carbon prices in the Guangdong pilot market and eight influencing factors from three perspectives (the international carbon market, energy prices, and China’s economic situation), using the ARMA-GARCH-vine copula model. The CoVaR between the carbon price and each factor is then calculated using copula-CoVaR. The results show that the crude oil market plays the primary role in the vine structure, and that the carbon market is not strongly correlated with other markets. China’s carbon market is still a regional market driven by government policy, and the international carbon and energy markets (especially the crude oil market) have upward risk spillover effects upon it. This indicates an asymmetric risk spillover between influencing factors and the carbon market. The findings of this study will help market participants prepare risk management strategies and make related investment decisions, and provide a reference for policy makers to formulate national emission trading scheme policies. Graphical abstract

“…Third, researchers have made predictions on carbon trading prices, such as Zhu et al [15], Sun et al [16] and Zhou and Wang [17], who all constructed new models to predict carbon trading prices, combining EDM, LSSVM, VMD, GA, BP and BES to create more accurate and effective carbon trading price prediction models. In addition, Hao and Tian [18] analyzed the role of the crude oil price, electricity price, exchange rate and AQI in predicting carbon trading prices, which is beneficial for investors to predict carbon trading risk. Zhao et al [19] argued that the U.S. Treasuries, Stoxx50, S&P Clean Energy Factor, and Brent oil prices in financial and energy markets contribute to carbon trading price forecasts.…”

Section: Literature Reviewmentioning

confidence: 99%

A Study of the Influencing Factors on the Carbon Emission Trading Price in China Based on the Improved Gray Relational Analysis Model

Song

et al. 2022

Sustainability

Carbon emission trading market construction is an important policy tool to promote the realization of China’s “double carbon” goal. However, problems still exist, such as the lack of market trading vitality, the large difference in carbon trading prices between the eight pilot markets and the instability of the prices. In order to explore the key influencing factors on carbon trading prices, 15 factors were selected to study in detail according to the policy, green industry, economy and environment. Taking China’s eight pilot carbon trading markets as research subjects, we explored the correlation degree of each factor by using the improved gray relational analysis model (GRAM) from the two dimensions of space and time. The research results show that from the space dimension,the industrial development level, development degree of low-carbon industries, air pollution degree and green technology maturity are the main factors that affect the carbon trading price in the eight pilot areas. Meanwhile, from the time dimension, the correlation degree between various factors and carbon trading price both showed a downward trend as a whole, and the fluctuation of the correlation degree of individual factors was different from the overall trend. In conclusion, we can put forward recommendations on the pricing mechanism of the carbon trading market after this comprehensive study.