The right place for the right job in the photovoltaic life cycle.

The potential for photovoltaic power generation (PV) to reduce primary energy consumption (PEC) and CO(2) emissions depends on the physical locations of each stage of its life cycle. When stages are optimally located, CO(2) emissions are reduced nearly ten times as much as when each stage is located in the country having the largest current market share. The usage stage contributes the most to reducing CO(2) emissions and PEC, and total CO(2) emissions actually increase when PV is installed in countries having small CO(2) emissions from electricity generation. Global maps of CO(2) reduction potential indicate that Botswana and Gobi in Mongolia are the optimal locations to install PV due to favorable conditions for PV power generation and high CO(2) emissions from current electricity generation. However, the small electricity demand in those countries limits the contribution to global CO(2) reduction. The type of PVs has a small but significant effect on life cycle PEC and CO(2) emissions.

Effect of temperature on PV potential in the world.

This work aims to identify the geographic distribution of photovoltaic (PV) energy potential considering the effect of temperature on PV system performance. A simple framework is developed that uses the JIS C 8907 Japanese industrial standard to evaluate the effects of irradiation and temperature on PV potential. The global distributions of PV potential and yearly performance ratio are obtained by this framework. Generally, the performance ratio decreases with latitude because of temperature. However, regions with high altitude have higher performance ratios due to low temperature. The southern Andes, the Himalaya region, and Antarctica have the largest PV potentials. Although PV modules with less sensitivity to temperature are preferable for the high temperature regions, PV modules that are more responsive to temperature may be more effective in the low temperature regions. The correlation between the estimates obtained by our framework and results from a more data-intensive method increases when the temperature effects are considered.