29307 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 29307 Zip Code | South Carolina | U.S. |
Total Housing Units | 8,311, 100% | 1,795,715 | 116,211,092 |
Utility Gas | 2,152, 25.89%, see rank | 23.19% | 48.85% |
Bottled, Tank, or LP Gas | 351, 4.22%, see rank | 4.45% | 4.86% |
Electricity | 5,425, 65.27%, see rank | 69.42% | 36.68% |
Fuel Oil, Kerosene, etc. | 242, 2.91%, see rank | 1.31% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.02% | 0.12% |
Wood | 106, 1.28%, see rank | 1.13% | 2.12% |
Solar Energy | 0, 0.00%, see rank | 0.01% | 0.05% |
Other Fuel | 9, 0.11%, see rank | 0.11% | 0.47% |
No Fuel Used | 26, 0.31%, see rank | 0.35% | 1.00% |
ACS 2008-2012 data
| 29307 Zip Code | South Carolina | U.S. |
Total Housing Units | 8,412, 100% | 1,768,255 | 115,226,802 |
Utility Gas | 2,296, 27.29%, see rank | 24.20% | 49.42% |
Bottled, Tank, or LP Gas | 491, 5.84%, see rank | 4.95% | 5.03% |
Electricity | 5,191, 61.71%, see rank | 67.56% | 35.51% |
Fuel Oil, Kerosene, etc. | 312, 3.71%, see rank | 1.75% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.01% | 0.12% |
Wood | 75, 0.89%, see rank | 1.12% | 2.08% |
Solar Energy | 0, 0.00%, see rank | 0.01% | 0.04% |
Other Fuel | 0, 0.00%, see rank | 0.10% | 0.43% |
No Fuel Used | 47, 0.56%, see rank | 0.30% | 0.90% |
US Census 2000 data
| 29307 Zip Code | South Carolina | U.S. |
Total Housing Units | 8,381, 100% | 1,533,854 | 105,480,101 |
Utility Gas | 2,131, 25.43%, see rank | 26.24% | 51.22% |
Bottled, Tank, or LP Gas | 759, 9.06%, see rank | 8.56% | 6.52% |
Electricity | 4,612, 55.03%, see rank | 58.36% | 30.35% |
Fuel Oil, Kerosene, etc. | 796, 9.50%, see rank | 5.11% | 8.97% |
Coal or Coke | 10, 0.12%, see rank | 0.01% | 0.14% |
Wood | 41, 0.49%, see rank | 1.26% | 1.68% |
Solar Energy | 0, 0.00%, see rank | 0.03% | 0.04% |
Other Fuel | 13, 0.16%, see rank | 0.14% | 0.39% |
No Fuel Used | 19, 0.23%, see rank | 0.29% | 0.69% |
* ACS stands for U.S. Census American Community Survey. According to the U.S. Census, if the date is a range, you can interpret the data as an average of the period of time.