The closest target-setting models based on data envelopment analysis (DEA) provide substantial contributions to benchmarking for requiring minimal effort to improve the performance of decision-making units (DMUs). However, these target-setting models may become impractical when several real factors, such as technology level, environmental influence, and governmental policy, are considered. In this study, we develop a novel DEA approach to target setting and benchmarking path selection by considering such real factors. A new bounded-change target-setting approach based on context-dependent DEA, in which the distance of evaluated unit to the best-practice frontier is minimized, is proposed. This approach sets benchmarks for inefficient DMUs in the short term and selects a realistic benchmarking path in the long term. Our approach also suggests a cross-level benchmarking path that few DEA-based benchmarking studies concern. This approach is applied to set sequential benchmarks and identify realistic benchmarking paths for inefficient DMUs via energy efficiency evaluation in the transport sector of China.