Potsdam Institute for Climate Impact Research, Potsdam, Germany.
Mercator Research Institute On Global Commons and Climate Change, Berlin, Germany.
Sci Rep. 2021 Apr 21;11(1):8571. doi: 10.1038/s41598-021-87714-w.
To design incentives towards achieving climate mitigation targets, it is important to understand the mechanisms that affect individual climate decisions such as solar panel installation. It has been shown that peer effects are important in determining the uptake and spread of household photovoltaic installations. Due to coarse geographical data, it remains unclear whether this effect is generated through geographical proximity or within groups exhibiting similar characteristics. Here we show that geographical proximity is the most important predictor of solar panel implementation, and that peer effects diminish with distance. Using satellite imagery, we build a unique geo-located dataset for the city of Fresno to specify the importance of small distances. Employing machine learning techniques, we find the density of solar panels within the shortest measured radius of an address is the most important factor in determining the likelihood of that address having a solar panel. The importance of geographical proximity decreases with distance following an exponential curve with a decay radius of 210 meters. The dependence is slightly more pronounced in low-income groups. These findings support the model of distance-related social diffusion, and suggest priority should be given to seeding panels in areas where few exist.
为了设计实现气候缓解目标的激励措施，了解影响个人气候决策（如太阳能电池板安装）的机制非常重要。已经表明，同伴效应在确定家庭光伏装置的采用和传播方面很重要。由于地理数据粗糙，尚不清楚这种影响是通过地理接近度还是通过具有相似特征的群体内部产生的。在这里，我们表明地理接近度是太阳能电池板实施的最重要预测因素，并且随着距离的增加，同伴效应会减弱。我们使用卫星图像为弗雷斯诺市构建了一个独特的地理位置数据集，以确定小距离的重要性。通过采用机器学习技术，我们发现地址最短测量半径内的太阳能电池板密度是确定该地址是否安装太阳能电池板的最重要因素。随着距离的增加，地理接近度的重要性呈指数衰减，衰减半径为 210 米。在低收入群体中，这种依赖性略为明显。这些发现支持与距离相关的社会扩散模型，并表明应优先在太阳能电池板数量较少的地区播种。
