African Swine Fever Virus (ASFV) is the cause of an infectious disease in pigs, which is difficult to control. Long viability of ASFV has been shown for several contaminated materials, especially under low temperature. Therefore, when pigs are exposed to a contaminated environment, new infections could occur without the presence of infectious individuals. For example, a contaminated, poorly washed, empty livestock vehicle poses a risk to the next load of pigs. A quantitative stochastic environmental transmission model was applied to simulate the change in environmental contamination levels over time and calculate the epidemic parameters through exposure-based estimation. Due to the lack of experimental data on environmental transmission at low temperatures, we performed a non-linear fit of the decay rate parameter with temperature based on a literature review. Eventually, 16 scenarios were constructed for different temperature (at 20 °C, 10 °C, 0 °C, or −10 °C) and duration of empty periods (1, 3, 5, or 7 days) after the environment had been contaminated. We quantified the variation in the contamination level of the environment over time and the probability of newly added recipients getting infected when exposed to the environment after the empty period. As a result, the transmission rate parameter for ASFV in pigs was estimated to be 1.53 (0.90, 2.45) day^-1, the decay rate parameter to be 1.02 (0.73, 1.47) day^-1 (at 21 °C), and the excretion rate parameter to be 2.70 (2.51, 3.02) day^-1. Without washing and disinfecting, the environment required 9, 14, 24, 54 days to reach a low probability of causing at least one new case (<0.005) at 20 °C, 10 °C, 0 °C, −10 °C, respectively. In addition, the method proposed in this paper enables assessment of the effect of washing and disinfecting on ASFV environmental transmission. We conducted this study to better understand how the viability of ASFV at different temperatures could affect the infectivity in environmental transmission and to improve risk assessment and disease control strategies