An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

Abstract

Abstract:In this paper, an improved grey model and scenario analysis, GA-GM(1,N) is proposed to forecast the carbon intensity in the Pearl River Delta (PRD) region, one of the most developed regions in China. Moreover, to show the advantage and feasibility of the proposed model, the forecasting results of the GA-GM(1,N) model are compared with that of a single-variable grey model (GM (1,1)) and a multivariable form (GM(1,N)). Data from one sample period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) are used to develop the models, and data from another sample period (2013)(2014)(2015) are used to test them. The mean absolute percentage error (MAPE) is applied to measure the accuracy of prediction. The results show that, of the three models, GA-GM(1,N) produces the best carbon intensity forecasts, with MAPEs of 0.4-1.4% and 0.04-0.4% in the development and testing periods respectively. This indicates that the optimization of the genetic algorithm is effective. The realization of carbon reduction targets in different cities is also explored by combining grey models with scenario analysis. Only Guangzhou could achieve its reduction target under all scenarios, and it can serve as a reference for other cities. Policy recommendations are provided based on these results.

İtalya’daki CO2 Emisyon Tahmini için Veri Madenciliği

“…A data mining technique was used to study climate change results from vehicles by analyzing fuel consumption and passenger vehicle emissions [15], predict carbon prices [16], and forecast CO2 emissions for China with consideration given to its population [17]. Another study used data mining to forecast the main influencing factors of CO2 emissions is Grey Model (GM) [18]. Data mining used in low-carbon management, the discovery of evolving patterns of climate change, predict climate change impacts, remote-sensing, economic, technological, and management approaches to combat climate change, exploitation of trans-Arctic maritime transportation [19,20].…”

Carbon emissions dynamic simulation and its peak of cities in the Pearl River Delta Urban Agglomeration

“…[17,23,36~38] , 其包含人口规模、人均GDP、能源强度和 能源消费碳强度四大碳排放关键参数, 其拥有分解无 残差、对碳排放变化推动因素解释力强、不依赖历史 碳排放数据等优点. 相较于利用神经网络 [21] 、灰色模 型 [39] 、多元线性回归 [13~14,20] 等方法建立融合碳排放量 与参数的预测函数, Kaya模型与蒙特卡罗法相结合的 预测模型因无需根据历史碳排放数据调节模型系数, 所以新冠肺炎疫情等重大事件对模型影响较小, 已被 广泛应用于城市碳排放情景研究中 [37,38] . 同时由于城 市能源消费数据统计制度不一、口径有变、指标差异 等问题, 多城市碳排放情景研究更是大多选用这四大 城市间统计口径一致的参数建立城市碳排放量预测模 型 [20,39] , 其预测模型、历史碳排放值、碳排放预测值 等均能通过不确定性检验, 说明基于此四大参数开展 多城市碳排放情景研究具有可行性.…”

“…相较于利用神经网络 [21] 、灰色模 型 [39] 、多元线性回归 [13~14,20] 等方法建立融合碳排放量 与参数的预测函数, Kaya模型与蒙特卡罗法相结合的 预测模型因无需根据历史碳排放数据调节模型系数, 所以新冠肺炎疫情等重大事件对模型影响较小, 已被 广泛应用于城市碳排放情景研究中 [37,38] . 同时由于城 市能源消费数据统计制度不一、口径有变、指标差异 等问题, 多城市碳排放情景研究更是大多选用这四大 城市间统计口径一致的参数建立城市碳排放量预测模 型 [20,39] , 其预测模型、历史碳排放值、碳排放预测值 等均能通过不确定性检验, 说明基于此四大参数开展 多城市碳排放情景研究具有可行性. Kaya恒等式的公 蒙特卡罗法能按照一定概率分布对多个变量进行 随机取值与组合, 并对组合后的变量与模型进行运算, 具有多情景组合研究与不确定性分析的模型优势 [17] .…”

“…与主流的多元线性回归 [13,14,20] 、神经网络 [21] 、灰色模 型 [39] 此外, Mann-Kendall趋势检验能对非正态分布数据进行 趋势分析且不受少数异常值干扰, 被广泛应用于城市 尺度气候序列趋势研究 [11,40] , 本文进一步利用其评估 某时段内城市碳排放量变化趋势, 公式为…”

A bi-objective programming model for carbon emission quota allocation: Evidence from the Pearl River Delta region

“…Because the prediction of GM (1, 1) is based on the internal growth trend of the data, it can be regarded as a natural growth of carbon emissions without policy constraints. The main prediction steps of GM (1, 1) can be found in Ye et al (2018)'s work. Moreover, the mean absolute percentage error ( ) of participant can be calculated as follow:…”