The sustainable development of agriculture is important for China, where more than 20 percent of the world population live. Many factors affect the sustainable development of agriculture in China. The purpose of this paper is to find out what factors play influential roles on affecting the growth pattern. Based on a modified production function model built upon Slow (1956) and Romer (2001), the impact of growth drag on the agricultural economy, and the contribution resources, are evaluated empirically using a time-series regression analysis based on the case of China for the period 1978–2015. The estimated coefficient of the total agricultural growth drag in China is 1.32, which suggests that the annual agricultural growth is associated with a 1.32 percent decline due to the scarcities of land, water, and energy. Specifically, the growth drags from land, water, and energy are found to be 0.5 percent, 0.44 percent, and 0.38 percent, respectively. Additionally, the results suggest that capital is the most important factor in the agricultural sector, although agriculture is highly dependent on land. Capital contributes around 76.86 percent to the growth of the agricultural economy, whereas contributions from land, energy, water, and technological progress are relatively small. Therefore, more investment should be added to agriculture, to accelerate the technology progress. Furthermore, the saving of water and energy is also important for the sustainable development of agriculture.

The economic damage function of the integrated assessment model (IAM) is used to quantify the economic loss caused by climate change, and it is quite important for coupling the economic system and natural system in a model. However, there are many shortcomings of the damage function in the current IAM. The most important shortcoming is that the only climatic factor in the damage function is the global annual mean temperature rise, which would result in the estimation deviating from reality. We improved the damage function by introducing extreme climate change events. Additionally, the improved damage function is applicable to the regional characteristics by using regional climate change data. The extreme climate event information is extracted with the categories of the disasters. The damage function is set up by using the Ordinary Least Square (OLS) method based on the climate data and economic loss data. The results show that the improved IAM damage function can better reflect the actual climate change economic loss in China, making it more reasonable. This paper provides an important method and technical solution for the realization of an effective connection between the disaster damage function of the integrated assessment model and the climate elements of the Earth system model.