In Canada, greenhouse gas (GHG) emissions continue to remain high despite the federal government's commitment to reduce GHG emissions. Environmental policy across Canadian provinces has largely lacked consistency with some provinces implementing targeted policy such as carbon taxes while others have strongly resisted the federal government's attempts to encourage such policy. This study examines the impact of environmental policy on GHG gas emissions across Canadian provinces with policy uniquely measured by public and business sector spending on environmental protection as well as notable province-specific policies. An extended STIRPAT model is specified with the inclusion of proxies for environmental policy using panel data from Canada's ten provinces from 1995 to 2019. The cointegration and the error correction modelling approaches are employed to estimate the non-stationary panel data. A pooled mean group estimation is applied to provide short run and long run coefficients. The findings indicate that although per capita GDP is the main driver of GHG emissions in Canada, environmental policy has a significant mitigating effect, as shown by the negative association between emissions and private and public sector spending on environmental protection in the long run. Furthermore, provinces with targeted policies, such as Ontario's coal phase-out and British Columbia's carbon tax, experienced significant emissions reductions. The theoretical implication is the successful addition of policy variables to the STIRPAT model, providing the model a new application for future research.

中文翻译：

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环境保护政策对温室气体排放的有效性

在加拿大，尽管联邦政府承诺减少温室气体排放，但温室气体 (GHG) 排放量仍然居高不下。加拿大各省的环境政策在很大程度上缺乏一致性，一些省份实施了碳税等有针对性的政策，而另一些省份则强烈抵制联邦政府鼓励此类政策的尝试。本研究探讨了加拿大各省环境政策对温室气体排放的影响，政策以公共和商业部门的环境保护支出以及值得注意的具体省份政策为唯一衡量标准。使用 1995 年至 2019 年加拿大十个省的面板数据，指定了扩展的 STIRPAT 模型，其中包含环境政策代理。采用协整和误差校正建模方法来估计非平稳面板数据。应用合并平均组估计来提供短期和长期系数。研究结果表明，尽管人均GDP是加拿大温室气体排放的主要驱动因素，但环境政策具有显着的缓解效果，从长期来看，排放量与私营和公共部门环境保护支出之间的负相关关系就表明了这一点。此外，实施有针对性政策的省份，例如安大略省的煤炭淘汰和不列颠哥伦比亚省的碳税，排放量大幅减少。理论意义是STIRPAT模型成功添加了政策变量，为模型未来的研究提供了新的应用。