• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China
【2h】

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China

机译：基于NLS的非线性灰色多元模型预测中国的污染物排放

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N)) model based on the nonlinear least square (NLS) method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N) and the NLS-based TNGM (1, N) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N) model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly.
机译：污染物排放与经济增长之间的关系一直是环境经济学的主要研究重点。为了准确估算中国污染物排放随经济增长的非线性变化规律，本研究基于非线性最小二乘（NLS）方法建立了转换后的非线性灰色多变量（TNGM（1，N））模型。基于NLS基本原理，使用Gauss-Seidel迭代算法求解TNGM（1，N）模型的参数。该算法通过连续迭代并不断逼近非线性模型的最佳回归系数来提高模型的精度。在我们的经验分析中，分别采用传统的灰色多元模型GM（1，N）和基于NLS的TNGM（1，N）模型来预测和分析人均废水排放量（WDPC）与人均排放量之间的关系。 1996年至2015年期间，中国的二氧化硫和粉尘排放量以及人均国内生产总值。结果表明，NLS算法能够有效地帮助灰色多变量模型识别污染物排放与经济增长之间的非线性关系。结果表明，与传统的GM（1，N）模型相比，基于NLS的TNGM（1，N）模型在预测WDPC，SO2排放量和人均粉尘排放量时具有更高的精度； WDPC显示出与GDP增长一致的增长趋势，而人均SO2和粉尘排放量则相应减少。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号