Increased recycling and reuse rates are a central part of the objectives laid out by the COP21. Nonetheless, the practical implementation of what has been called the circular economy, as well as its true potential, are not easily established. This is because the impact and implementation time scales of any intervention depend on knowing the lifetime of products, which is frequently unknown. This is particularly true in construction, responsible for 39% of worldwide emissions, 11% of which are embodied. Most material flow analysis (MFA) models will simply assume a range of plausible life expectancies when bottom‐up data are lacking. In this work, we propose a novel method of identification using the high quality but highly aggregated trade data available and use it to establish a “mortality curve” for buildings and other long‐lasting products. This identification method is intended to provide more reliable inputs to existing MFA models. It is widely applicable because of the general availability of the underlying data. Using it on United Kingdom trade data, we identify product classes at 1 year for packaging/home scrap, 1 to around 10 years for vehicles/equipment, and around 50 years for construction. The identification approach was then validated by using classical approaches using bottom‐up data for vehicles.

中文翻译：

---

估算长寿命产品类别寿命的新方法

提高回收和再利用率是COP21提出的目标的核心部分。但是，很难轻易确定所谓的循环经济的实际实施及其真正的潜力。这是因为任何干预措施的影响和实施时间尺度都取决于了解产品的寿命，而这通常是未知的。在建筑业中尤其如此，占全球排放量的39％，其中11％是体现出来的。当缺乏自下而上的数据时，大多数材料流分析（MFA）模型只会简单地假设一系列合理的预期寿命。在这项工作中，我们提出了一种使用高质量但高度汇总的贸易数据进行识别的新颖方法，并将其用于为建筑物和其他持久产品建立“死亡率曲线”。这种识别方法旨在为现有MFA模型提供更可靠的输入。由于基础数据的一般可用性，因此它可广泛应用。使用它在英国的贸易数据上，我们确定产品类别为包装/家庭废料为1年，车辆/设备为1至10年左右，建筑为50年左右。然后，通过使用车辆自下而上数据的经典方法对识别方法进行验证。