Long-term monitoring of Sacramento Shade program trees: Tree survival, growth and energy-saving performance

•We assessed tree survivorship, growth, and energy performance over 22 years.•Survivorship was 42.4%, substantially lower than the initial projection.•Annual cooling saving per property and per tree were 23% and 52% of the initial projection.•Lower survivorship was the major factor affecting lower cooling savings.•Planting medium stature trees and rapidly growing large trees achieve the greatest long-term energy savings. Long-term survival and growth of urban forests are critical to achieve the targeted benefits of urban tree planting programs, such as building energy savings from tree shade. However, little is known about how trees perform in the long-term, especially in residential areas. Given this gap in the literature, we monitored 22-years of post-planting survival, growth, and energy saving performance of shade trees in Sacramento, California. Using field surveys, aerial photo interpretation and survival analysis, we calculated cumulative survivorship and compared measured with projected tree growth. Using Shadow Pattern Simulator and Micropas (building energy simulation), combined with survival and growth observations, we modeled the current energy savings produced by the program trees and then compared this result with initial projections from the early years of the program. The 22-year post planting survivorship was 42.4%, considerably less than the initial projection. On average, measured growth rates were within expected ranges to provide shading benefits; 22-year old trees reached 74.6% and 68.8% of the projected 30-year mature size for tree heights and crown diameters, respectively. Annual energy savings were 107kWh per property and 80kWh per tree, which were 23% and 52% of the initial projection, respectively. Lower survivorship was the primary factor influencing lower cooling savings. Medium-sized trees had higher survivorship and growth attainment compared to other trees. This study contributes to more accurate quantification of urban greening performance, helping urban forest managers make data-driven decisions.