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It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO–XGBOOST–CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO–XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO–XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO–XGBOOST–CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model's validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices. [ABSTRACT FROM AUTHOR]

Against the backdrop of severe global warming, the low-carbon city pilot policy, with carbon emission reduction as its main objective, is an important initiative for China to fulfil its international commitment to carbon emission reduction and promote a green and low-carbon development strategy. However, none of the literature has yet evaluated whether the pilot low-carbon city policy promotes urban land use efficiency as a policy effect. In view of this, this paper measures urban land use efficiency from a low-carbon perspective using a global reference super-efficiency SBM model based on data from 186 prefecture-level cities in China from 2005–2017, and subsequently constructs a difference-in-differences method to test the true impact of low-carbon city pilot policies on urban land use efficiency and carbon emissions, and uses a propensity score matching method to test its robustness. It is found that: (1) the average level of urban land use efficiency in China is low and on a downward trend; (2) overall, cities are predominantly low-efficiency cities, with only the high-efficiency cities in Guangdong Province showing spatial agglomeration; and (3) the low-carbon city pilot policy reduces carbon emissions while also negatively affecting urban land use efficiency. Accordingly, this paper puts forward corresponding policy recommendations. [ABSTRACT FROM AUTHOR]

In response to global warming, China has formulated the "double carbon" strategic goals of peaking carbon dioxide emissions before 2030 and reaching carbon neutrality before 2060. The problem of rural carbon emissions is often ignored due to underdeveloped industries and services. In this paper, the carbon emission pathways in the rural areas of Guangdong Province are investigated. Since energy consumption is the main source of carbon emissions, the factor analysis was used to analyze the main factors affecting rural household energy consumption and agricultural production energy consumption. Multiple linear regression was conducted to predict the rural energy consumption demand in Guangdong. Furthermore, the current situation and development trend of rural energy supply, demand and consumption structure, and the potential of renewable energy development were considered to predict carbon emissions in the rural areas of Guangdong. Moreover, the carbon emission pathways in the rural areas of Guangdong were discussed under two scenarios: the base scenario and the radical model. [ABSTRACT FROM AUTHOR]

Global warming caused by carbon emissions has become increasingly prominent. As the world's second-largest economy, China is under enormous pressure to cut down its carbon dioxide emissions. It is urgent to seek effective methods to forecast carbon emissions and put forward the pointed and effective measures to reduce emissions. In this paper, we first use the software Eviews to make an analysis of randomness on data of carbon emissions in the four representative provinces and city, Beijing, Henan, Guangdong, and Zhejiang, in terms of their carbon emissions data from 1997 to 2017. Then, according to their distinct characteristics, the methods of stationary processing of the difference, moving average, and substituting strong impact points, respectively, are adopted to perform the data preprocessing. Then, model identification, parameter estimation, and model test are carried out to establish the model of ARIMA(p , d, q) for the prediction of the carbon emissions of the four regions, respectively. Finally, the model is used to forecast the data and analyze their tendency for their carbon emissions in the next three years. The results can provide guidance for decision-makers to set reasonable carbon emission reduction targets and take appropriate energy conservation and emission reduction measures. [ABSTRACT FROM AUTHOR]

Against the backdrop of global climate warming, the issue of flash flood disasters in small watersheds triggered by heavy rainfall is gradually becoming more prominent. Selecting an appropriate hydrological model is crucial for flash flood disaster defense. This article focuses on the Daxi Water Basin in Lianping County, Guangdong Province, as the research area. Firstly, organize the data and subject it to standardization processing. Subsequently, establish the topological relationships within the basin, construct a hydrological model for simulating flood processes in Chinese mountainous regions, and obtain a set of model parameters applicable to the specific basin. The results indicated that: ① the relative errors of flood runoff depth were all less than 7%, with an average of 4.5%; ② the relative errors of peak flow for all events were less than 6%, with an average of 4.2%; ③ peak time errors were all within ±2 h, either earlier or later than the actual peak by 1 h; ④ the Nash-Sutcliffe efficiency coefficient for floods were all greater than 0.8, with an average of 0.86. The research results above will serve as a reference and guidance for flood defense management in the Daxi Water Basin. [ABSTRACT FROM AUTHOR]

In recent years, coastal areas have been threatened by many potential hazards due to global warming, glacier melting and sea level rise. Understanding their evolutionary history and development trends can help predict disasters and further reduce the corresponding losses. The Hanjiang River delta in the southeastern part of China is the second largest delta in Guangdong Province and has such challenges. Studying the sedimentary evolution and delta initiation of the Hanjiang Delta is beneficial for understanding the response of the Hanjiang Delta to present and future sea level and climate changes. In this research, we drilled a series of cores from the Hanjiang subaqueous delta, which contains information on the sedimentary environment, climate change and sea level change during the late Quaternary. Combined with previous research results and under the constraint of high-precision and high-resolution AMS14C and OSL, we carried out a multi-proxy analysis that included micropaleontology and grain size to obtain information on the sedimentary environment, sea level change and climate change. We then further discussed the initiation of the Hanjiang delta and its primary factors. The Quaternary sediments began depositing in the early Late Pleistocene (MIS5), and three sedimentary cycles can be recognized from bottom to top. The dating results also indicate that the first two cycles were formed during the late Pleistocene, while the last cycle was formed during the Holocene. The initiation of the Hanjiang Delta was indicated by a progradation in the process of a transition from estuary to a typical delta. At this time, the rate of delta progradation seaward was fast, and increasing amounts of sediments moved through the third line of islands into the sea. The barrier–lagoon system began to develop in the estuary of Hanjiang during this period. With the sequential construction of the delta, the lagoon was filled and covered by delta deposition, and the barrier bar moved to the sea; thus, the barrier-coast delta depositional model was established in the study area. Since the last glacial period (LGM), the Hanjiang River Delta and other river deltas in the region seem to have experienced similar evolutionary histories, including the filling of incised paleo-valleys and estuaries in the Early Holocene and deltaic progradation in the Middle to Late Holocene, controlled by sea level change. [ABSTRACT FROM AUTHOR]

Previous studies show that the environment in the Guangdong, Hong Kong, and Macao region is under the double stress of global warming and urbanization. Here, we show that due to the increase of regional greenness, the effect of urbanization warming on surface air temperature (SAT) decreased with time and became statistically insignificant from 2004 to 2018, compared to 1979 onward; while the urbanization itself has significantly warmed land surface temperature (LST), with a warming rate of 0.14°C ± 0.04°C/10a at daytime and 0.02°C ± 0.02°C/10a at nighttime during 2004–2018, respectively. The anthropogenic heat was found to have a limited influence on SAT, but more significant and tangible effects on LST. It is essential to improve the control of additional warming effects caused by urbanization. Plain Language Summary: The Guangdong, Hong Kong, and Macao region is the most economically developed region in China, and the urbanization impact on the regional warming is frequently discussed during recent decades. We found that the urbanization warming on surface air temperature (SAT) has decreased to insignificant during the recent decade due to regional greening, while the warming of land surface temperature (LST) remains tangible. This result is related to the different effects of two moderating drivers (anthropogenic heat and vegetation index) on both SAT and LST. Thus, it is essential to increase urban greenness and reduce anthropogenic heat fluxes in parallel. Key Points: The effect of urbanization on the surface air temperature (SAT) decreases over time as regional vegetation greening increasesThe urbanization effect on the land surface temperatures (LSTs) from the long time series of satellite retrievals remains significantThe anthropogenic heat was found to have a limited influence on SAT, but more significant and tangible effects on LST [ABSTRACT FROM AUTHOR]