Religious language continues to permeate contemporary discourse, even in ostensibly secular domains such as environmental activism and climate change debates. This paper investigates how explicit and implicit forms of religious language appear in climate-related texts produced by secular and religious nongovernmental organizations (NGOs). We introduce a dual methodological approach: a rule-based model using a hierarchical tree of religious terms derived from ecotheology literature, and large language models (LLMs) operating in a zero-shot setting. Using a dataset of more than 880,000 sentences, we compare how these methods detect religious language and analyze points of agreement and divergence. The results show that the rule-based method consistently labels more sentences as religious than LLMs. These findings highlight not only the methodological challenges of computationally detecting religious language but also the broader tension over whether religious language should be defined by vocabulary alone or by contextual meaning. This study contributes to digital methods in religious studies by demonstrating both the potential and the limitations of approaches for analyzing how the sacred persists in climate discourse.

翻译：宗教语言持续渗透于当代话语之中，即便在环境行动主义和气候变化辩论等表面世俗的领域亦然。本文研究了显性与隐性的宗教语言形式如何出现在由世俗及宗教非政府组织（NGO）所产出的气候相关文本中。我们引入了一种双重方法论路径：一种是基于规则的方法，使用源自生态神学文献的宗教术语层级树模型；另一种是大型语言模型（LLMs）在零样本设定下的应用。基于一个包含超过88万句子的数据集，我们比较了这些方法检测宗教语言的效果，并分析了一致与分歧点。结果表明，基于规则的方法持续地将更多句子标记为含有宗教语言，其数量超过LLMs的判定。这些发现不仅凸显了通过计算手段检测宗教语言所面临的方法论挑战，也揭示了关于宗教语言应仅由词汇定义还是需结合上下文意义来界定的更广泛张力。本研究通过展示分析神圣性如何在气候话语中持续存在的研究路径的潜力与局限，为宗教研究领域的数字方法做出了贡献。