Central Asia is one of the most sensitive regions to climate changes in the world and the grassland degradation of this region has attracted considerable concern. Quantifying the driving force of grassland degradation is important for understanding the effects of climate variation and human activities on grassland. In this study, net primary productivity (NPP) was selected as an indicator to quantitatively evaluate the relative role of climate variation and human activities in Central Asia from 2000 to 2020. This study used the global NPP product MOD17A3 as actual NPP and estimated the potential NPP using the Thornthwaite memorial model. The potential NPP and the difference between the potential NPP and actual NPP were used to represent the influence of climate variation and human activities. The grassland degradation or restoration can be demonstrated by the slope of actual NPP (S_A). A positive slope value (S_A) suggested that restoration occurs, whereas a negative slope value suggested that degradation occurred. The results showed that 23.08% of the total grassland area experienced grassland degradation, whereas 2.51% of the whole grassland underwent grassland restoration. Furthermore, 53.8% of the degraded grassland areas were influenced by climate variation, and 14.5% were caused by human activities. By contrast, the relative roles of climate variation and human activities in grassland restoration were 25% and 47.9%, respectively. The NPP variation also could be calculated by assessing the effects of these factors and the results showed that 55.7% of the NPP decrease was caused by climate variation, whereas 9.6% was a result of human activities. On the contrary, climate variation and human activities resulted in 19.8% and 37.3% of grassland restoration, respectively. Therefore, climate variation was the dominant factor of grassland degradation, and human activities were the main driver of grassland restoration in Central Asia.

The large-scale deployment of negative emission technologies (NETs) is critical for achieving carbon neutrality, but identifying an optimal portfolio that balances economic and environmental sustainability remains a challenge. In this study, a multi-objective model is proposed to identify the optimal NETs portfolio in Northwest China, a region characterized by energy wealth and ecological vulnerability. The developed model aims to simultaneously maximize the economic returns while minimizing environmental footprints, including land, water, energy, nitrogen, and phosphorus consumption. A dual discount rate framework, incorporating both economic and environmental discount rates, is established to analyze the tradeoffs between economy and environment. Results indicate that biochar is the most effective NET and dominates the technology portfolio before 2035. Conversely, bioenergy with carbon capture and storage shows limited effectiveness and sustainability due to its high environmental footprints. In the long term (post-2035), enhanced weathering and direct air carbon capture and storage become essential for achieving deep decarbonization and eventual carbon neutrality. Furthermore, results also reveal that higher economic and environmental discount rates promote the early deployment of NETs but result in a lower economic return and a larger environmental footprint, highlighting a trade-off between short-term action and long-term sustainability. These results provide useful information for formulating optimal NETs deployment strategies and underscore the necessity of incorporating long-term environmental concerns into policy-making to ensure the sustainable achievement of carbon neutrality.