To achieve Sustainable Development Goal (SDG) 7, prioritizing the socially disadvantaged segments of the population is imperative, given their inherent susceptibility to heightened risks of energy exclusion. However, a comprehensive understanding of the diverse energy challenges faced by households with socio-economic disparities remains elusive. This article thus addresses this gap by examining three widely acknowledged categories of marginalized households in India: racial inferiority, income poverty, and gender inequality. It notably pioneers the quantification of an umbrella pattern of energy deprivation within the SDG7 framework, encompassing energy unaffordability, energy unreliability, energy inaccessibility, and energy inequality. To do so, leveraging the latest household survey dataset and employing least squares estimates, we preliminarily capture that these three disadvantaged groups encounter significant energy barriers in the pursuit of SDG7 achievement. Given respectively selected models based on Least Absolute Shrinkage and Selection Operator (LASSO) approach, the gradient boosting model (GBM), another state-of-the-art machine learning technique, is subsequently adopted to verify feature significance and rank its importance in determining diverse energy deprivation faced by each group. The results reveal that the disadvantaged caste groups and those experiencing greater gender inequality encounter the greatest impediments to their right to reliable energy access. In comparison, energy unaffordability poses a paramount challenge for low-income households. These findings enable policymakers to design straightforward interventions that address a spectrum of socio-economic disparities, thereby fostering an just energy transition grounded in data-driven evidence.

中文翻译：

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以 SDG7 为目标：使用机器学习识别印度社会弱势群体的异质能源困境


为了实现可持续发展目标 （SDG） 7，优先考虑社会弱势群体势在必行，因为他们天生就容易受到能源排斥风险的侵袭。然而，要全面了解存在社会经济差异的家庭面临的各种能源挑战仍然难以捉摸。因此，本文通过研究印度三个广为公认的边缘化家庭类别来解决这一差距：种族劣等、收入贫困和性别不平等。值得注意的是，它在 SDG7 框架内率先量化了能源匮乏的总括模式，包括能源负担不起、能源不可靠、能源无法获取和能源不平等。为此，利用最新的住户调查数据集并采用最小二乘估计，我们初步捕捉到这三个弱势群体在追求 SDG7 成就的过程中遇到了重大的能源障碍。给定基于最小绝对收缩和选择运算符 （LASSO） 方法的分别选择模型，随后采用另一种最先进的机器学习技术梯度提升模型 （GBM） 来验证特征显着性并对其在确定每个群体面临的不同能量剥夺方面的重要性进行排序。结果显示，弱势种姓群体和性别不平等程度较高的群体在获得可靠能源的权利方面遇到了最大的障碍。相比之下，能源负担不起对低收入家庭构成了最大的挑战。 这些发现使政策制定者能够设计出直接的干预措施来解决一系列社会经济差异，从而促进基于数据驱动证据的公正能源转型。