Terrestrial ecosystem carbon storage (TECS) could significantly affect the concentration of atmospheric CO2, which is critical for climate change prediction. Along these lines, the Integrated Valuation of Ecosystem Services and Trade-offs model was employed to determine the TECS of Hainan Island (HN) from 2015 to 2050 accurately. Besides, the Future Land-use Simulation model combined with natural and anthropogenic factors was used to forecast the land-use types from 2025 to 2050 in HN by considering different Shared-socioeconomic pathway-Rrepresentative concentration pathway (SSP-RCP) scenarios. Finally, the geographical detector explored the influence mechanism concerning the TECS. Under the SSP1-RCP1.9 scenario, the TECS of HN will be gradually increased to 388.10 million tons in 2050, mainly due to the increase in forest areas and the fact that the majority of grassland in the western part of HN is being converted into forest. Under different SSP-RCP scenarios except for SSP1-RCP1.9, HN's TECS is expected to gradually decrease from 2015 to 2050, mainly due to the loss of grassland and forest in coastal low-altitude areas. From the single/pair factor perspective influenced mechanism concerning the TECS, the elevation (DEM) and DEM∩Slope were found to be the dominant single/pair factor under the SSP1-RCP1.9, SSP1-RCP2.6 and SSP2-RCP4.5 scenarios. The least distance to residential area (LDP) and LDP∩LDR (i.e. LDP and least distance to roads or railways) were found to be the dominant factors under the SSP3-RCP7.0, SSP4-RCP3.4, SSP4-RCP6.0, SSP5-RCP3.4 and SSP5-RCP8.5 scenarios. Besides, the pair factors provided a higher determinant power for TECS than a single factor. Given the results of the TECS and the influence mechanism concerning the TECS under different SSP-RCP scenarios, we suggest reasonably planning the transportation network and limiting the disorderly expansion of construction land. 

Simulation and attribution analysis of terrestrial ecosystem carbon storage of Hainan Island from 2015 to 2050.

Explore the spatial pattern of carbon emissions in urban functional zones: a case study of Pudong, Shanghai, China.

Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

I am pleased that my review article has stimulated such broader and thoughtful discussions in probability theory, theoretical statistics, estimation methods, and applications. The discussants have ...

Rejoinder of “On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures”

Identifying systemic risk patterns in geopolitical, economic, financial, environmental, transportation, epidemiological systems, and their impacts is the key to risk management. This paper introduces two new endopathic and exopathic competing risks. The paper integrates the new extreme value theory for maxima of maxima and the autoregressive conditional Frechet model for systemic risk into a new autoregressive conditional accelerated Frechet (AcAF) model, which enables decoupling systemic risk into endopathic and exopathic competing risks. The paper establishes the probabilistic properties of stationarity and ergodicity of the AcAF model. Statistical inference is developed through conditional maximum likelihood estimation. The consistency and asymptotic normality of the estimators are derived. Simulation demonstrates the efficiency of the proposed estimators and the AcAF model's flexibility in modeling heterogeneous data. Empirical studies on the stock returns in S\&P 500 and the cryptocurrency trading show the superior performance of the proposed model in terms of the identified risk patterns, endopathic and exopathic competing risks, being informative with greater interpretability, enhancing the understanding of the systemic risks of a market and their causes, and making better risk management possible.

Decoupling Systemic Risk into Endopathic and Exopathic Competing Risks Through Autoregressive Conditional Accelerated Fr\'echet Model

We congratulate Professor Zhengjun Zhang for a topnotch contribution to the literature and thank the editor for the invitation to participate in the discussion of the excellent review paper. Zhang ...

Application of autoregressive tail-index model to China's stock market

This paper proposes a novel regional carbon emission inequality (RCI) index based on a special kind of general distribution. Using the proposed RCI index and based on China’s county-level panel data over the time span of 1997–2017, the regional carbon emission inequality of China is evaluated at intra-provincial, sub-national, and national levels. Based on that, the dependence between regional carbon inequality and carbon efficiency is studied by using copula functions and nonlinear dependence measures. The empirical results show that: (1) Shanghai, Tianjin, and Inner Mongolia have the worst carbon inequalities; while Hainan, Qinghai, and Jiangxi are the three most carbon-equal provinces; (2) there is a divergence phenomenon in RCI values of municipalities over the past decade; (3) from the national-level perspective, the inter-provincial carbon emission inequality is much greater than that at the intra-provincial level; (4) from the sub-national-level perspective, the east region has the highest RCI value, followed by the northeast, west, and the central regions; (5) there is a so-called "efficiency–equality (E-E) trade-off" in each provincial administrative unit, meaning that the higher carbon efficiency generally comes with higher carbon inequality, i.e., carbon efficiency comes at a price of carbon inequality; and (6) by re-grouping provincial units via the efficiency–equality cost and industrial structure, respectively, both carbon equality and carbon efficiency can be achieved in some regions simultaneously, thereby getting out of the “E-E trade-off” dilemma. The empirical evidence may provide valuable insight regarding the topic of “equality and efficiency” in environmental economics, and offer policy implications for regional economic planning and coordination.

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Evaluating Regional Carbon Inequality and Its Dependence with Carbon Efficiency: Implications for Carbon Neutrality

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Application of autoregressive tail-index model to China's stock market

Evaluating Regional Carbon Inequality and Its Dependence with Carbon Efficiency: Implications for Carbon Neutrality