Twenty-one typical coupled large samples were chosen from areas within and surrounding nature reserves on the Tibetan Plateau using the large sample comparison method (LSCM). To evaluate the effectiveness of the nature reserves in protecting the ecological environment, the alpine grassland net primary production (NPP) of these coupled samples were compared and the differences between them before and after their establishment as protected areas were analyzed. The results showed that: (1) With respect to the alpine grassland NPP, the ecological and environmental conditions of most nature reserves were more fragile than those of the surrounding areas and also lower than the average values for the Tibetan Plateau. (2) Of the 11 typical nature reserves selected, the positive trend in the NPP for Manzetang was the most significant, whereas there was no obvious trend in Taxkorgan. With the exception of Selincuo, the annual NPP growth rate in the nature reserves covered by alpine meadow and wetland was higher than that in nature reserves consisting of alpine steppe and alpine desert. (3) There were notable findings in 21 typical coupled samples: (a) After the establishment of the nature reserves, the annual rate of increase in the NPP in 76% of samples inside nature reserves and 82% of samples inside national nature reserves was higher than that of the corresponding samples outside nature reserves. (b) The effectiveness of ecological protection of the Mid-Kunlun, Changshagongma, Zoige and Selincuo (Selin Co) nature reserves was significant; the effectiveness of protection was relatively significant in most parts of the Sanjiangyuan and Qiangtang nature reserves, whereas in south-east Manzetang and north Taxkorgan the protection effectiveness was not obvious. (c) The ecological protection effectiveness was significant in nature reserves consisting of alpine meadow, but was weak in nature reserves covered by alpine steppe. This study also shows that the advantage of large sample comparison method in evaluating regional ecology change. Careful design of the samples used, to ensure comparability between the samples, is crucial to the success of this LSCM.

In the context of Beijing’s accelerated economic growth, a high urbanization rate and associated urban problems pose challenges. We collected panel data for the period 2005–2014 to examine the relationship between Beijing’s urbanization efficiency and economic growth rate as well as its spatial patterns of dynamic and static urbanization efficiency. Specifically, we developed a comprehensive index system for assessing Beijing’s economic growth rate and urbanization efficiency at the district (county) level. Economic level was selected as an indicator of the economic growth rate. Economic urbanization and consumption levels were selected as indicators of urbanization efficiency. We applied a sequential Malmquist total factor productivity index to estimate the dynamic urbanization efficiency and economic growth rate at the district/country level from 2005 to 2014. We measured Beijing’s static urbanization efficiency in 2014 using a data envelopment analysis model and assessed its spatiotemporal dynamics and urbanization efficiency pattern using a Getis–Ord General G_i index. The results indicated an overall average increase of 1.07% in the total factor urbanization efficiency (TFUE), with an average value of 0.91, while the total factor economic growth rate (TFEE) remained stable at an average value of 0.979. The low TFUE level evidently continues to significantly constrain TFEE. Both TFUE and TFEE levels in the Capital Function Core (CFC) area were significant, exhibiting high inputs and outputs, while these levels in the Urban Function Development (UFD), City Development Zone (CDZ), and Ecological Conservation Development (ECD) areas were below 1 for most periods, strongly indicating inefficient factor allocation. In view of this spatial pattern, TFUE’s regional spatial distribution appears remarkable, showing a decreasing trend from north to south in Beijing, excluding CFC areas. During the period 2005–2014, the CFC area and northeastern Beijing gradually developed into high urbanization efficiency cluster regions. The dominant factors accounting for the difference in total factor productivity indices between TFUE and TFEE were technical change (TC) and scale efficiency change (SEC), and the main factors driving the regional spatial distribution pattern for urbanization efficiency were TC and technical efficiency change (TEC). Accordingly, local governments should promote TC, SEC, and TEC to improve urbanization levels, with optimal strategies entailing strengthening policy support and encouraging investments in technology in UFD, CDZ, and ECD areas. Within Beijing, Dongcheng, Xicheng, Shijingshan, Mentougou, and Yanqing demonstrated effectively balanced static urbanization efficiency levels in 2014, whereas these levels in the city’s remaining 11 districts were not optimal, with extensive development. County governments should therefore promote efforts to reduce input redundancy and improve pure technical efficiency to maintain sustainable and steady development.