The industrial sector contributes significantly to the world energy consumption and emissions of greenhouse gases. Pursuing actions to keep a global temperature rise this century within 1.5 degrees Celsius, as part of the Paris agreement, will require drastic action from all sectors of the national economies, including the industrial sector. In this context, econometric studies are useful to help explore the pathways of energy and fuel consumption which can be expected if historical drivers continue to unfold in the way they did in the recent past. Econometric studies of industrial energy demand are however surprisingly scarce. Studies for industrial subsectors are even more so, with evidence starting to be built recently and for a very limited number of countries. On the other hand, the topic of heterogeneity has gained more and more importance in energy economics, as testified by contributions taking into account the impact of this factor topics such as energy efficiency and the rebound effect. In fact, heterogeneity in the industrial subsector fuel demand is key to understand fuel substitution, its impact on business-as-usual scenarios used as a starting point for climate mitigation, as well as the strength of the levers available to policy maker to help the industrial sector in the decarbonizing transition. As future climate commitments become more stringent, the importance of understanding subsectorial of fuel substitution becomes more valuable. At the same time, as longer time series become available, econometric studies assessing heterogeneity in the industrial subsectors become more and more viable. This paper show that useful empirical evidence on this subject can be obtained by applying a parsimonious multivariate cointegration analysis that makes use of the readily available time series data on fuel demand and its determinants. We estimate fuel demand by incorporating dynamic specifications typical of cointegration studies and the system approach typical of translog studies. We model fuel demand as shares in a cointegrating VAR system with as many cointegrating vectors as the number of modelled fuels, each representing the long-run demand for a specific fuel. Our approach presents a number of important advantages. Firstly, we are able to model the simultaneous determination of demand for different fossil fuels within a consistent framework. Secondly, we can exploit the cross-equation restrictions implied by the long-run representation, which offer a useful means to reduce the number of parameters to estimate. Finally, additional gain in terms of degrees of freedom is ensured by the fact that we model shares rather than fuel intensities. Our main result, the emergence of substantial differences in the systematic behavior of firms across subsectors, provides a note of caution to authors imposing homogeneity in the fuel demands across subsectors, estimating fuel share elasticities for the industrial sector as a whole or focusing on energy consumption rather than fuel consumption. In addition, we find that price elasticities in the UK industrial sector are larger than many previous estimates available in the literature, and we confirm that gas consumption is more sensitive to price variations than electricity consumption. These conclusions are important not only from a modelling perspective, in a way which we would expect to be replicated for other countries, but also for policy-making

中文翻译：

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英国工业分部门的燃料需求

工业部门对世界能源消耗和温室气体排放做出了重大贡献。作为巴黎协议的一部分，采取行动将本世纪全球气温上升幅度控制在 1.5 摄氏度以内，需要包括工业部门在内的国民经济所有部门采取激烈行动。在这种情况下，计量经济学研究有助于探索能源和燃料消耗的路径，如果历史驱动因素继续以最近过去的方式展开，则可以预期这些路径。然而，对工业能源需求的计量经济学研究却出奇地稀缺。对工业分部门的研究更是如此，最近开始为极少数国家建立证据。另一方面，异质性主题在能源经济学中变得越来越重要，正如考虑到能源效率和反弹效应等该因素主题影响的贡献所证明的那样。事实上，工业子行业燃料需求的异质性是理解燃料替代、其对作为减缓气候变化起点的一切照旧情景的影响以及决策者可用于帮助工业部门在脱碳转型。随着未来气候承诺变得更加严格，了解燃料替代子行业的重要性变得更加重要。与此同时，随着更长的时间序列变得可用，评估工业子部门异质性的计量经济学研究变得越来越可行。本文表明，可以通过应用简约的多元协整分析获得关于该主题的有用经验证据，该分析利用关于燃料需求及其决定因素的现成时间序列数据。我们通过结合典型的协整研究的动态规范和典型的 translog 研究的系统方法来估计燃料需求。我们将燃料需求建模为协整 VAR 系统中的份额，协整向量与建模燃料的数量一样多，每个向量代表对特定燃料的长期需求。我们的方法具有许多重要的优势。首先，我们能够在一致的框架内模拟对不同化石燃料的需求的同时确定。其次，我们可以利用长期表示所隐含的交叉方程限制，这提供了一种有用的方法来减少要估计的参数数量。最后，我们对份额而不是燃料强度进行建模这一事实确保了在自由度方面的额外收益。我们的主要结果，即跨子行业的公司系统行为出现的重大差异，为作者提供了一个注意事项，即强加跨子行业燃料需求的同质性，估计整个工业部门的燃料份额弹性或关注能源消耗而不是油耗。此外，我们发现英国工业部门的价格弹性大于文献中许多先前的估计，并且我们确认天然气消费对价格变化比电力消费更敏感。