Based on China’s new development philosophy, and following the idea of “assessing the overall levels firstly, then anchoring the subsystems contributions and finally identifying the obstacle factors”, an evaluation system of high-quality development (HQD) levels is constructed from five subsystems of innovative development (ID), coordinated development (CD), green development (GD), open development (OD) and shared development (SD). In this paper, the entropy weight TOPSIS model is used to measure the HQD levels of 19 provincial units located in the Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB) in 2010–2019, and with the assistance of the Obstacle Degree Model, the obstacle factors affecting the HQD of both are depicted. The results show that: (1) between 2010 and 2019, the HQD indices in YRB and YREB have been on an increasing trend, and the subsystems indices all show a trend of “GD > CD > SD > ID > OD”, but the overall levels of HQD in both is not high, and there is still much room for improvement. (2) YREB has a significantly higher HQD level than YRB. From 2010 to 2019, the average values of the HQD indices in YRB and YREB were 0.2886 and 0.4073, respectively, but the index in YRB improves at a faster pace than YREB, and the gap of HQD levels between the two show a trend of continuous reduction towards regional balance and coordination. (3) The analysis of obstacle factors shows that while OD and ID are the main obstacles to HQD in both basins, the impact of both on the resistance to HQD in both basins is on a decreasing trend. GD is another bottleneck for the HQD of YRB, and the pressure on GD in the middle and upper reaches of the provincial units in the basin is increasing, and the obstacles are on a rising trend.

Positioned in the era of the transformation of China's primary social contradictions, this study delves into the new connotations of regional coordinated development(RCD) from the perspective of “factors” coordination within the region and constructs an RCD evaluation system from five subsystems of regional economic coordination(REC), urban-rural coordination(URC), economic and social coordination(EASC), resource and environmental coordination(RAEC), and material and spiritual civilization coordination(MASCC). Then, the Entropy weight-TOPSIS model is used to evaluate the RCD levels of the 19 provinces located in the Yangtze River Economic Belt(YREB) and Yellow River Basin(YRB) from 2010 to 2019, and the two-way fixed-effects model is employed to illustrate the driving mechanisms of various influencing factors on the RCD in YRB and YREB. The results show that:(1)the RCD levels of YRB and YREB show a fluctuating upward trend during 2010 and 2019, however, both regions have low RCD levels, as seen by the mean RCD indices for YREB and YRB, which are only 0.433 and 0.309, respectively. (2) The RCD level of YREB is higher than that of YEB. In 2019, the “coordinated” provinces in YRB and YREB account for 37.50% and 81.82% of the total number of provinces in the basins, respectively, the “uncoordinated” and “low coordinated” provinces all located in YRB. (3) The RCD of YRB and YREB is significantly improved by REC, URC and RAEC, but not significantly positively by MASCC or EASC, and insufficient development of MASCC is the main contradiction limiting the increase in the RCD level of YRB, while the low level of EASC has become the main obstacle limiting the RCD of YREB. (4)Finally, based on the varying impact degrees and directions of different influencing factors on the RCD in YRB and YREB, the recommendations to promote RCD are proposed.

Based on a new development philosophy, this paper constructs an index system for measuring and evaluating high-quality developments in the Yellow River Basin using the entropy weight TOPSIS model and the obstacle degree analysis model to carry out a diagnostic analysis of the spatio-temporal differences in the level of high-quality development and obstacle factors in nine Yellow River Basin provinces from 2010 to 2020. The results show that: (1) During the period 2010–2020, the Yellow River Basin’s high-quality development index showed a rising trend and the subindexes showed a trend of “coordinated development > shared development > green development > open development > innovative development”. However, the overall level of high-quality development in the basin is not high, and there is still much room for improvement. (2) The global Moran’s I of the high-quality development indexes in 2010, 2015 and 2020 are −0.216, −0.204 and −0.103, respectively, indicating that the level of high-quality development in each province and region within the Yellow River Basin is discrete and develops in a spatially unbalanced direction. (3) The obstacle degree analysis shows that green development, innovation development and open development are the main bottlenecks affecting the high-quality development of the basin. The restrictive role of green development is increasingly prominent, and the low level of scientific and technological innovation and achievement transformation, serious resource consumption and low resource utilization rate of industrial development, in addition to the low level of openness to the outside world are becoming key factors hindering the high-quality development of the provinces in the Yellow River Basin.

Urban heat risk depends not only on thermal environment intensity but also on the spatiotemporal distribution and diurnal rhythm of population activity. Conventional assessments based on single-phase land surface temperature or static population data cannot capture intraday exposure variation or differences among functional spaces. Using a selected high-temperature day (13 June 2025) in Zhengzhou, this paper builds a Dynamic Thermal Environment Index (DTEI) fusing a static Landsat-derived thermal baseline with intraday variation from Himawari-9. It couples DTEI with a Population Activity Index (PAI) from hourly Baidu heat-map data to construct a 500 m grid-scale Dynamic Heat Exposure Index (DHEI). DHEI’s diurnal rhythm, spatial pattern, and functional-space differences are analyzed, and a two-factor decomposition identifies the hourly relative contributions of DTEI and PAI. Results show that: (1) DTEI, DHEI, and PAI peak at 13:00, 14:00, and 18:00, with DHEI lagging DTEI by 1 h and leading PAI by 4 h; (2) high-DHEI areas concentrate in the central built-up area with a stable spatial pattern, expanding outward and rising in level over the day; (3) exposure intensity follows commercial and residential > public service > transportation, green/recreational, and employment/industrial zones, with markedly different peak timings between groups; and (4) DHEI variation is driven not by a single factor but by alternating thermal environment and population activity contributions, with the peak formed by their joint effect in early afternoon. Dynamic urban heat exposure is thus a time-varying outcome of thermal environment and population activity coupling, offering a useful reference for time- and zone-specific heat-risk management.